2020

  • Asong, Z., Elshamy, M., Princz, D., Wheater, H., Pomeroy, J., Pietroniro, A., & Cannon, A. (2020). High-resolution meteorological forcing data for hydrological modelling and climate change impact analysis in the Mackenzie River Basin. Earth System Science Data, 12(1): 629-645, doi: 10.5194/essd-12-629-2020.
  • Bajracharya, A. R., Awoye, O. H. R., Stadnyk, T. A., and Asadzadeh, M. 2020. Time Variant Sensitivity Analysis of Hydrological Model Parameters in a Cold Region Using Flow Signatures. Water, 12(4), 961.
  • Brunner, M. I., Melsen, L. A., Wood, A. W., Rakovec, O., Mizukami, N., Knoben, W. J. M., & Clark, M. P. (in-review, 2020). Flood hazard and change impact assessments may profit from rethinking model calibration strategies. Hydrology and Earth System Sciences.
  • Budhathoki, S., Rokaya, P., Lindenschmidt, K. E. (2020). Improved modelling of a Prairie catchment using a progressive two-stage calibration strategy with in situ soil moisture and streamflow data. Hydrology Research, 51(3), 505-520, doi: https://doi.org/10.2166/nh.2020.109.
  • Budhathoki, S., Rokaya, P., Lindenschmidt, K. E., & Davison, B. (2020). A multi-objective calibration approach using in-situ soil moisture data for improved hydrological simulation of the Prairies. Hydrological Sciences Journal, 65(4), 638-649, doi: 1080/02626667.2020.1715982.
  • Cholette, M., J. M. Thériault, J. A. Milbrandt and H. Morrison (2020). Impacts of predicting the liquid fraction of mixed-phase particles on the simulation of an extreme freezing rain event: the 1998 Ice Storm, Monthly Weather Review (Conditionally accepted).
  • Das, A., Rokaya, P., & Lindenschmidt, K. E. (2020). Ice-Jam Flood Risk Assessment and Hazard Mapping under Future Climate. Journal of Water Resources Planning and Management, 146(6), 04020029.
  • Do, N. C., & Razavi, S. (2020). Correlation effects? A major but often neglected component in sensitivity and uncertainty analysis. Water Resources Research, e2019WR025436.
  • Eamen, L., Brouwer, R. and Razavi, S. (2020). The economic impacts of water supply restrictions due to climate and policy change: a transboundary river basin supply-side input-output analysis. Ecological Economics https://doi.org/10.1016/j.ecolecon.2019.106532
  • Elshamy, M. E., Princz, D., Sapriza-Azuri, G., Abdelhamed, M. S., Pietroniro, A., Wheater, H. S., & Razavi, S. (2020). On the configuration and initialization of a large-scale hydrological land surface model to represent permafrost. Hydrology and Earth System Sciences, 24(1), 349-379.
  • Fabris, L., Rolick, R.L., Kurylyk, B.L., Carey, S.K. (2020). Characterization of contrasting flow and thermal regimes in two adjacent subarctic alpine headwaters in northwestern Canada. Hydrological Processes, doi: 10.1002/hyp.13786.
  • Fowler, K., Knoben, W., Peel, M., Peterson, T., Ryu, D., Saft, M., ... & Western, A. (2020). Many commonly used rainfall‐runoff models lack long, slow dynamics: implications for runoff projections. Water Resources Research, e2019WR025286.doi: 10.1029/2019WR025286.
  • Garcia, J. & Brouwer, R. (2020) A Multiregional Input-Output Optimization Model to Assess the Economic Impacts of Water Supply Disruptions in the Great Lakes Basin, Economic Systems Research. Accepted, pending revisions.
  • Glass, B., Rudolph, D. and Duguay, C. (2020). Identifying Groundwater Discharge Zones in Northern Canada Using Remotely Sensed Optical and Thermal Imagery. Canadian Journal of Earth Sciences. In revision (submission number: cjes-2019-0169).
  • Huang, P., de Rochambeau, D., Sleiman, H., Liu, J. (2020). Target Self-enhanced Selectivity in Metal-specific DNAzymes, Angewandte Chemie International Edition, 132, 3601-3605. doi: 10.1002/anie.201915675
  • Huang, X., Rudolph, D.L., and Glass, B. (2020), Potential influence of roadbed frost load on water main failure, Geophysical Research Letters (in final revision)
  • Gharari, S., Clark, M. P., Mizukami, N., Knoben, W. J. M., Wong, J. S., and Pietroniro, A. (2020). Flexible vector-based spatial configurations in land models, Earth Syst. Sci. Discuss., doi:10.5194/hess-2020-111, in review.
  • Li, H., Zhong, X., Ma, Z., Tang, G., Ding, L., Sui, X., ... & He, Y. (2020). Climate Changes and Their Teleconnections with ENSO Over the Last 55 Years, 1961–2015, in Floods‐Dominated Basin, Jiangxi Province, China. Earth and Space Science, 7(3), e2019EA001047.
  • Li, Z., Chen, M., Gao, S., Hong, Z., Tang, G., Wen, Y., ... & Hong, Y. (2020). Cross-Examination of Similarity, Difference and Deficiency of Gauge, Radar and Satellite Precipitation Measuring Uncertainties for Extreme Events Using Conventional Metrics and Multiplicative Triple Collocation. Remote Sensing, 12(8), 1258.
  • Lu, X., Tang, G., Wang, X., Liu, Y., Wei, M., & Zhang, Y. (2020). The Development of a Two-Step Merging and Downscaling Method for Satellite Precipitation Products. Remote Sensing, 12(3), 398, doi: 3390/rs12030398.
  • Lv, Z., & Pomeroy, J. W. (2020). Assimilating snow observations to snow interception process simulations. Hydrological Processes, 34(10), 2229-2246. doi: 10.1002/hyp.13720.
  • Marsh, C., Pomeroy, J., & Wheater, H. (2020). The Canadian Hydrological Model (CHM) v1.0: a multi-scale, multi-extent, variable-complexity hydrological model - design and overview. Geoscientific Model Development, 13: 225-247, doi: 10.5194/gmd-13-225-2020.
  • Marsh, C., Pomeroy, J., Spiteri, R., & Wheater, H. (2020). A finite volume blowing snow model for use with variable resolution meshes. Water Resources Research, 56(2): 1-28, doi: 10.1029/2019WR025307.
  • Moon, W. J, Liu, J. (2020) Replacing Mg2+ by Fe2+ for RNA-cleaving DNAzymes, ChemBioChem, 21, 401-407. doi: 10.1002/cbic.201900344
  • Nguyen, H., Barzegar, R., Khosravi, K., Omidvar, E., Quilty, J., Kheyrollah Pour, H., Taghi Aalami, M., Nhu, V. (2020). Snow Water Equivalent Prediction in a Mountainous Area using Hybrid Bagging Machine Learning Approaches. Journal of Hydrology. HYDROL-S-20-01378 (under review).
  • Razavi, S., Gober, P., Maier, H. R., Brouwer, R., & Wheater, H. (2020). Anthropocene flooding: Challenges for science and society. Hydrological Processes, 34(8), 1996-2000. https://doi.org/10.1002/hyp.13723
  • Rokaya, P., & Lindenschmidt, K. E. (2020). Correlation among parameters and boundary conditions in river ice models. Modeling Earth Systems and Environment, 6(1), 499-512.
  • Rokaya, P., Morales-Marin, L., & Lindenschmidt, K.-E. (2020). A physically based modelling framework for operational forecasting of river ice breakup. Advances in Water Resources, 139, 103554. https://doi.org/https://doi.org/10.1016/j.advwatres.2020.103554
  • Rokaya, P., Peters, D., Elshamy, M., Budhathoki, S., & Lindenschmidt, K. E. (2020) Impacts of future climate on the hydrology of a northern headwaters basin and its implications for a downstream deltaic ecosystem. Hydrological Processes, 34 (7), 1630– 1646. https://doi.org/10.1002/hyp.13687
  • Scott, A. K., Xu, L., Kheyrollah Pour, H. (2020). Retrieval of ice/water observations from synthetic aperture radar imagery for use in lake ice data assimilation. Journal of Great Lakes Research. GLR-D-19-00296 (under review).
  • Sigmund, G., Gharasoo, M., Hüffer, T., & Hofmann, T. (2020). Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials. Environmental Science & Technology, 54(7), 4583-4591. doi: 10.1021/acs.est.9b06287
  • Slaughter, A. R. and Razavi, S. (2020). Paleo-hydrologic reconstruction of 400 years of past flows at a weekly time step for major rivers of Western Canada, Earth Syst. Sci. Data, 12, 231–243, https://doi.org/10.5194/essd-12-231-2020.
  • Tang, G., Clark, M. P., Newman, A. J., Wood, A. W., Papalexiou, S. M., Vionnet, V., and Whitfield, P. H. (2020). SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018, Earth Syst. Sci. Data Discuss., in review, https://doi.org/10.5194/essd-2020-92.
  • Tang, G., Clark, M. P., Papalexiou, S. M., Ma, Z., & Hong, Y. (2020). Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets. Remote Sensing of Environment, 240, 111697, doi: 1016/j.rse.2020.111697.
  • Wang, Y., Liu, T., & Liu, J. (2020). Synergistically Boosted Degradation of Organic Dyes by CeO2 Nanoparticles with Fluoride at low pH. ACS Applied Nano Materials, 3, 842-849. doi: 10.1021/acsanm.9b02356
  • Wiebe, A. J., & Rudolph, D. L. (2020). On the sensitivity of modelled groundwater recharge estimates to rain gauge network scale. Journal of Hydrology, 585, 124741. https://doi.org/10.1016/j.jhydrol.2020.124741.
  • Williams, B. S., Luo, B. & Lindenschmidt, K.-E. (2020). An ice-jam flood hazard assessment of a lowland river and its terminus inland delta. Natural Hazards, in-review
  • Zhang, Z., Li, Y., Barlage, M., Chen, F., Miguez-Macho, G., Ireson, A., and Li, Z. (2020). Modeling groundwater responses to climate change in the Prairie Pothole Region, Earth Syst. Sci., 24, 655–672. https://doi.org/10.5194/hess-24-655-2020.

2019

  • Armstrong, R., Pomeroy, JW., & Martz, L. (2019). Spatial variability of mean daily estimates of actual evaporation from remotely sensed imagery and surface reference data. Hydrology and Earth System Sciences, 23(12): 4891-4907. doi: 10.5194/hess-23-4891-2019.
  • Bennett, A., Nijssen, B., Ou, G., Clark, M., & Nearing, G. (2019). Quantifying process connectivity with transfer entropy in hydrologic models. Water Resources Research, 55(6), 4613-4629. doi: 10.1029/2018WR024555.
  • Blöschl, G., Bierkens, M. F., Chambel, A., Cudennec, C., Destouni, G., Fiori, A., ... & Stumpp, C. (2019). Twenty-three unsolved problems in hydrology (UPH)–a community perspective. Hydrological Sciences Journal, 64(10), 1141-1158, doi:10.1080/02626667.2019.1620507.
  • Bocaniov, S. A., Van Cappellen, P., & Scavia, D. (2019). On the role of a large shallow lake (Lake St. Clair, USA‐Canada) in modulating phosphorus loads to lake erie. Water Resources Research, 55, 10548– 10564. https://doi.org/10.1029/2019WR025019.
  • Carr, M. K., Sadeghian, A., Lindenschmidt, K. E., Rinke, K., & Morales-Marin, L. (2019). Impacts of Varying Dam Outflow Elevations on Water Temperature, Dissolved Oxygen, and Nutrient Distributions in a Large Prairie Reservoir. Environmental Engineering Science, 37:1, 78-97.
  • Chegwidden, O. S., Nijssen, B., Rupp, D. E., Arnold, J. R., Clark, M. P., Hamman, J. J., ... & Pan, M. (2019). How do modeling decisions affect the spread among hydrologic climate change projections? Exploring a large ensemble of simulations across a diversity of hydroclimates. Earth's Future, 7(6), 623-637. doi: 10.1029/2018EF001047.
  • Cholette, M., Morrison, H., Milbrandt, J. A., & Thériault, J. M. (2019). Parameterization of the bulk liquid fraction on mixed-phase particles in the Predicted Particle Properties (P3) scheme: Description and Idealized simulations. Journal of the Atmospheric Sciences, 76(2), 561-582.
  • Costa, C., Baulch, H., Elliott, J., Pomeroy, J., & Wheater, H. (2019). Modelling nutrient dynamics in cold agricultural catchments: A review. Environmental Modelling and Software, 124: 1-16, doi: 10.1016/ j.envsoft.2019.104586.
  • Costa, D., Pomeroy, J., Baulch, H., Elliott, J., & Wheater, H. (2019). Using an inverse modelling approach with equifinality control to investigate the dominant controls on snowmelt nutrient export. Hydrological Processes, 33(23): 2958-2977, doi: 10.1002/hyp.13463.
  • Fan, Y., Clark, M., Lawrence, D. M., Swenson, S., Band, L. E., Brantley, S. L., ... & Kirchner, J. W. (2019). Hillslope hydrology in global change research and Earth system modeling. Water Resources Research, 55(2), 1737-1772. doi: 10.1029/2018WR023903.
  • Fang, X., Pomeroy, JW., DeBeer, C.., Harder, P., & Siemens, E. (2019). Hydrometeorological data from Marmot Creek Research Basin, Canadian Rockies. Earth System Science Data, 11(2): 455-471, DOI: 10.5194/essd-11-455-2019.
  • Gharari, S., Clark, M., Mizukami, N., Wong, J. S., Pietroniro, A., & Wheater, H. (2019). Improving the representation of subsurface water movement in land models. Journal of Hydrometeorology, 20 (12): 2401–2418. doi: 10.1175/JHM-D-19-0108.1.
  • Guillaume, J. H., Jakeman, J. D., Marsili-Libelli, S., Asher, M., Brunner, P., Croke, B., ... & Stigter, J. D. (2019). Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose. Environmental Modelling & Software, 119 (11): 418-432.
  • Krinner, G., Derksen, C., Essery, R., Flanner, M., Hagemann, S., Clark, M., ... & Ménard, C. B. (2018). ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks. Geoscientific Model Development, 11, 5027-5049. doi: 10.5194/gmd-11-5027-2018.
  • Kurkute, S., Li, Zhenhua., Li, Yanping., & Huo, F. (2019). Assessment and Projection of Water Budget over Western Canada using Convection Permitting WRF Simulations. Earth Syst. Sci. Discuss., 1–32. https://doi.org/10.5194/hess-2019-522
  • Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., et al. (2019). The Community Land Model version 5: Description of new features, benchmarking, and impact of forcing uncertainty. Journal of Advances in Modeling Earth Systems, 11, 4245– 4287. https://doi.org/10.1029/2018MS001583
  • Lilhare, R., Déry, S. J., Pokorny, S., Stadnyk, T. A., & Koenig, K. A. (2019). Intercomparison of Multiple Hydroclimatic Datasets across the Lower Nelson River Basin, Manitoba, Canada. Atmosphere-Ocean, 57(4), 262-278.
  • Lehner, F., Wood, A. W., Vano, J. A., Lawrence, D. M., Clark, M. P., & Mankin, J. S. (2019). The potential to reduce uncertainty in regional runoff projections from climate models. Nature Climate Change, 9(12), 926-933. doi: 10.1038/s41558-019-0639-x.
  • Chen, L., Li, Y., Chen, F., Barlage, M., Zhang, Z., & Li, Z. (2019). Using 4-km WRF CONUS simulations to assess impacts of the surface coupling strength on regional climate simulation. Climate Dynamics, 53(9-10), 6397-6416. doi:10.1007/s00382-019-04932-9
  • Du, L., Xu, L., Li, Y., Liu, C., Li, Z., Wong, J. S., & Lei, B. (2019). China’s Agricultural Irrigation and Water Conservancy Projects: A Policy Synthesis and Discussion of Emerging Issues. Sustainability, 11(24), 7027.
  • Lindenschmidt, K. E., Carr, M. K., Sadeghian, A., & Morales-Marin, L. (2019). CE-QUAL-W2 model of dam outflow elevation impact on temperature, dissolved oxygen and nutrients in a reservoir. Scientific Data, 6(1), 1-7.
  • Lindenschmidt, K. E., Carstensen, D., Fröhlich, W., Hentschel, B., Iwicki, S., Kögel, M., ... & Łoś, H. (2019). Development of an Ice Jam Flood Forecasting System for the Lower Oder River—Requirements for Real-Time Predictions of Water, Ice and Sediment Transport. Water, 11(1), 95.
  • Lindenschmidt, K. E., & Rokaya, P. (2019). A Stochastic Hydraulic Modelling Approach to Determining the Probable Maximum Staging of Ice-Jam Floods. Journal of Environmental Informatics, 34(1).
  • Lindenschmidt, K. E., Rokaya, P., Das, A., Li, Z., & Richard, D. (2019). A novel stochastic modelling approach for operational real-time ice-jam flood forecasting. Journal of Hydrology, 575, 381-394.
  • Scaff, L., Prein, A. F., Li, Y., Liu, C., Rasmussen, R., & Ikeda, K. (2019). Simulating the convective precipitation diurnal cycle in North America’s current and future climate. Climate Dynamics, 1-14. doi: 1007/s00382-019-04754-9.
  • Lv, Z., Pomeroy, JW., & Fang, X. (2019). Evaluation of SNODAS Snow Water Equivalent in Western Canada and Assimilation into a Cold Regions Hydrological Model. Water Resources Research, 55(12): 11166-11187, DOI:10.1029/2019WR025333.
  • Lv, & Pomeroy, J. (2019). Detecting intercepted snow on mountain needleleaf forest canopies using satellite remote sensing. Remote Sensing of Environment, 231, 1-19, DOI: 10.1016/j.rse.2019.111222.
  • Maier, H.R., Razavi, S., Kapelan, Z., Matott, L.S., Kasprzyk, J., and B.A. Tolson. (2019). Introductory overview: Optimization using evolutionary algorithms and metaheuristics. Environmental Modelling & Software, 114: 195-213. https://doi.org/10.1016/j.envsoft.2018.11.018
  • Melsen, L. A., Teuling, A. J., Torfs, P. J., Zappa, M., Mizukami, N., Mendoza, P. A., ... & Uijlenhoet, R. (2019). Subjective modeling decisions can significantly impact the simulation of flood and drought events. Journal of hydrology, 568, 1093-1104. doi: 10.1016/j.jhydrol.2018.11.046.
  • Mizukami, N., Rakovec, O., Newman, A. J., Clark, M. P., Wood, A. W., Gupta, H. V., and Kumar, R. (2019). On the choice of calibration metrics for “high-flow” estimation using hydrologic models, Earth Syst. Sci., 23, 2601–2614, https://doi.org/10.5194/hess-23-2601-2019.
  • Morales-Marín, L. A., Rokaya, P., Sanyal, P. R., Sereda, J., & Lindenschmidt, K. E. (2019). Changes in streamflow and water temperature affect fish habitat in the Athabasca River basin in the context of climate change. Ecological Modelling, 407, 108718.
  • Morales-Marin, L. A., Sanyal, P. R., Kadowaki, H., Li, Z., Rokaya, P., & Lindenschmidt, K. E. (2019). A hydrological and water temperature modelling framework to simulate the timing of river freeze-up and ice-cover breakup in large-scale catchments. Environmental modelling & software, 114, 49-63.
  • Newman, A. J., Clark, M. P., Longman, R. J., & Giambelluca, T. W. (2019). Methodological intercomparisons of station-based gridded meteorological products: Utility, limitations, and paths forward. Journal of Hydrometeorology, 20(3), 531-547. doi: 10.1175/JHM-D-18-0114.1.
  • Poirier, É., Thériault, J. M., & Leriche, M. (2019). Role of sublimation and riming in the precipitation distribution in the Kananaskis Valley, Alberta, Canada. Hydrology & Earth System Sciences, 23(10).
  • Popp, A. L., Lutz, S. R., Khatami, S., van Emmerik, T. H. M., & Knoben, W. J. M. (2019). A Global Survey on the Perceptions and Impacts of Gender Inequality in the Earth and Space Sciences. Earth and Space Science, 6(8), 1460–1468, doi: 10.1029/2019EA000706
  • Rakovec, O., Mizukami, N., Kumar, R., Newman, A., Thober, S., Wood, A. W., et al. (2019). Diagnostic evaluation of large‐domain hydrologic models calibrated across the contiguous United States. Journal of Geophysical Research: Atmospheres, 2019; 124: 13991– 14007. https://doi.org/10.1029/2019JD030767.
  • Rasouli, K., Pomeroy, JW., & Whitfield, PH. (2019). Are the effects of vegetation and soil changes as important as climate change impacts on hydrological processes? Hydrology and Earth System Sciences, 23(12): 4933-4954, DOI: 10.5194/hess-2019-214.
  • Rasouli, K., Pomeroy, J., & Whitfield, PH. (2019). Hydrological responses of headwater basins to monthly perturbed climate in the North American Cordillera. Journal of Hydrometeorology, 20(5): 863-882, DOI: 10.1175/JHM-D-18-0166.1.
  • Razavi, S., & Gupta, H. V. (2019). A multi-method Generalized Global Sensitivity Matrix approach to accounting for the dynamical nature of earth and environmental systems models. Environmental modelling & software, 114, 1-11.
  • Razavi, S., Sheikholeslami, R., Gupta, H. V., & Haghnegahdar, A. (2019). VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis. Environmental modelling & software, 112, 95-107.
  • Rokaya, P., Morales-Marín, L., Bonsal, B., Wheater, H., & Lindenschmidt, K. E. (2019). Climatic effects on ice phenology and ice-jam flooding of the Athabasca River in western Canada. Hydrological Sciences Journal, 64(11), 1265-1278.
  • Rokaya, P., Peters, D. L., Bonsal, B., Wheater, H., & Lindenschmidt, K. E. (2019). Modelling the effects of climate and flow regulation on ice‐affected backwater staging in a large northern river. River Research and Applications, 35(6), 587-600.
  • Sheikholeslami, R., Razavi, S., Gupta, H. V., Becker, W., & Haghnegahdar, A. (2019). Global sensitivity analysis for high-dimensional problems: How to objectively group factors and measure robustness and convergence while reducing computational cost. Environmental Modelling & Software, 111, 282-299.
  • Sheikholeslami, R., Razavi, S., and A Haghnegahdar. (2019). What should we do when a model crashes? Recommendations for global sensitivity analysis of Earth and environmental systems models. Geoscientific Model Development, 12 (10): 4275-4296. https://doi.org/10.5194/gmd-12-4275-2019.
  • Stewart, R. E., Szeto, K. K., Bonsal, B. R., Hanesiak, J. M., Kochtubajda, B., Li, Y., ... & Liu, Z. (2019). Summary and synthesis of Changing Cold Regions Network (CCRN) research in the interior of western Canada–Part 1: Projected climate and meteorology. Earth Syst. Sci. 23 (8), 3437–3455. https://doi.org/10.5194/hess-23-3437-2019
  • Stone, L.E., Fang, X., Haynes, K.M., Helbig, M., Pomeroy, J.W., Sonnentag, O., & Quinton, W.L. (2019). Modelling the effects of permafrost loss on discharge from a wetland-dominated, discontinuous permafrost basin. Hydrological Processes, 33(20): 2607-2626, doi: 2607-10.1002/hyp.13546.
  • Swenson, S. C., Clark, M., Fan, Y., Lawrence, D. M., & Perket, J. (2019). Representing intra‐hillslope lateral subsurface flow in the community land model. Journal of Advances in Modeling Earth Systems, 11, 4044– 4065. https://doi.org/10.1029/2019MS001833
  • Vano, J. A., Arnold, J. R., Nijssen, B., Clark, M. P., Wood, A. W., Gutmann, E. D., ... & Lehner, F. (2018). DOs and DON'Ts for using climate change information for water resource planning and management: guidelines for study design. Climate Services, 12, 1-13. doi: 10.1016/j.cliser.2018.07.002.
  • Wang, Y., Yang, J., Zhao Y. & Liu J. (2019) Intentional hydrolysis to overcome the hydrolysis problem: detection of Ce (IV) by producing oxidase-like nanozymes with F-", Chemical Communications, 55, 13434-13437. doi: 10.1039/C9CC06167C
  • Li, Y., Li, Z., Zhang, Z., Chen, L., Kurkute, S., Scaff, L., & Pan, X. (2019). High-resolution regional climate modeling and projection over western Canada using a weather research forecasting model with a pseudo-global warming approach. Earth Syst. Sci, 23(11), 4635-4659.
  • Yassin, F., Razavi, S., Elshamy, M., Davison, B., Sapriza-Azuri, G., & Wheater, H. (2019). Representation and improved parameterization of reservoir operation in hydrological and land-surface models. Earth Syst. Sci, 23(9), 3735-3764.
  • Zong, C., Liu, J., (2019) The Arsenic Binding Aptamer Cannot Bind Arsenic: Critical Evaluation of Aptamer Selection and Binding", Analytical Chemistry, 91, 10887-10893. doi: 10.1021/acs.analchem.9b02789