Articles | Volume 13, issue 1
https://doi.org/10.5194/gmd-13-225-2020
https://doi.org/10.5194/gmd-13-225-2020
Model description paper
 | 
29 Jan 2020
Model description paper |  | 29 Jan 2020

The Canadian Hydrological Model (CHM) v1.0: a multi-scale, multi-extent, variable-complexity hydrological model – design and overview

Christopher B. Marsh, John W. Pomeroy, and Howard S. Wheater

Related authors

Multi-scale snowdrift-permitting modelling of mountain snowpack
Vincent Vionnet, Christopher B. Marsh, Brian Menounos, Simon Gascoin, Nicholas E. Wayand, Joseph Shea, Kriti Mukherjee, and John W. Pomeroy
The Cryosphere, 15, 743–769, https://doi.org/10.5194/tc-15-743-2021,https://doi.org/10.5194/tc-15-743-2021, 2021
Short summary

Related subject area

Hydrology
The global water resources and use model WaterGAP v2.2e: description and evaluation of modifications and new features
Hannes Müller Schmied, Tim Trautmann, Sebastian Ackermann, Denise Cáceres, Martina Flörke, Helena Gerdener, Ellen Kynast, Thedini Asali Peiris, Leonie Schiebener, Maike Schumacher, and Petra Döll
Geosci. Model Dev., 17, 8817–8852, https://doi.org/10.5194/gmd-17-8817-2024,https://doi.org/10.5194/gmd-17-8817-2024, 2024
Short summary
Generalised drought index: a novel multi-scale daily approach for drought assessment
João António Martins Careto, Rita Margarida Cardoso, Ana Russo, Daniela Catarina André Lima, and Pedro Miguel Matos Soares
Geosci. Model Dev., 17, 8115–8139, https://doi.org/10.5194/gmd-17-8115-2024,https://doi.org/10.5194/gmd-17-8115-2024, 2024
Short summary
Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev., 17, 7751–7766, https://doi.org/10.5194/gmd-17-7751-2024,https://doi.org/10.5194/gmd-17-7751-2024, 2024
Short summary
Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL)
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024,https://doi.org/10.5194/gmd-17-7181-2024, 2024
Short summary
PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration
Matevž Vremec, Raoul A. Collenteur, and Steffen Birk
Geosci. Model Dev., 17, 7083–7103, https://doi.org/10.5194/gmd-17-7083-2024,https://doi.org/10.5194/gmd-17-7083-2024, 2024
Short summary

Cited articles

Ahrens, J., Geveci, B., and Law, C.: ParaView: An End-User Tool for Large Data Visualization, in Visualization handbook, Elsevier, 2005. 
Avanzi, F., Michele, C. D., Morin, S., Carmagnola, C. M., and Lejeune, Y.: Model complexity and data requirements in snow hydrology : seeking a balance in practical applications, Hydrol. Proc., 30, 2106–2118, https://doi.org/10.1002/hyp.10782, 2016. 
Bahremand, A.: HESS Opinions: Advocating process modeling and de-emphasizing parameter estimation, Hydrol. Earth Syst. Sci., 20, 1433–1445, https://doi.org/10.5194/hess-20-1433-2016, 2016. 
Bartelt, P. and Lehning, M.: A physical SNOWPACK model for the Swiss avalanche warning Part I: numerical model, Cold Reg. Sci. Technol., 35, 123–145, https://doi.org/10.1016/s0165-232x(02)00074-5, 2002. 
Bavay, M. and Egger, T.: MeteoIO 2.4.2: a preprocessing library for meteorological data, Geosci. Model Dev., 7, 3135–3151, https://doi.org/10.5194/gmd-7-3135-2014, 2014. 
Download
Short summary
The Canadian Hydrological Model (CHM) is a next-generation distributed model. Although designed to be applied generally, it has a focus for application where cold-region processes, such as snowpacks, play a role in hydrology. A key feature is that it uses a multi-scale surface representation, increasing efficiency. It also enables algorithm comparisons in a flexible structure. Model philosophy, design, and several cold-region-specific examples are described.