Articles | Volume 12, issue 3
https://doi.org/10.5194/tc-12-891-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-12-891-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
12 Mar 2018
Research article |
| 12 Mar 2018
Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models
Department of Earth, Ocean and Atmospheric Sciences, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
William W. Hsieh
Department of Earth, Ocean and Atmospheric Sciences, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Alex J. Cannon
Climate Research Division, Environment and Climate Change Canada, P.O. Box 1700 STN CSC, Victoria, BC V8W 2Y2, Canada
Markus A. Schnorbus
Pacific Climate Impacts Consortium, University House 1, 2489 Sinclair Road, University of Victoria, Victoria, BC V8N 6M2, Canada
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- Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry A. Horton et al. 10.3390/rs14246210
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- Development of a global operational snow analysis: The US Air Force Snow and Ice Analysis Y. Yoon et al. 10.1016/j.rse.2022.113080
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- Improving Snow Water Equivalent Maps With Machine Learning of Snow Survey and Lidar Measurements P. Broxton et al. 10.1029/2018WR024146
- Deep learning in environmental remote sensing: Achievements and challenges Q. Yuan et al. 10.1016/j.rse.2020.111716
- Exploring the Potential of Long Short‐Term Memory Networks for Improving Understanding of Continental‐ and Regional‐Scale Snowpack Dynamics Y. Wang et al. 10.1029/2021WR031033
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- Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach D. Shao et al. 10.5194/essd-14-795-2022
- SNOTEL, the Soil Climate Analysis Network, and water supply forecasting at the Natural Resources Conservation Service: Past, present, and future S. Fleming et al. 10.1111/1752-1688.13104
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- Evaluation of Remote Sensing and Reanalysis Snow Depth Datasets over the Northern Hemisphere during 1980–2016 L. Xiao et al. 10.3390/rs12193253
- Snow water equivalent prediction in a mountainous area using hybrid bagging machine learning approaches K. Khosravi et al. 10.1007/s11600-022-00934-0
- Characteristics of Snow Depth and Snow Phenology in the High Latitudes and High Altitudes of the Northern Hemisphere from 1988 to 2018 S. Yue et al. 10.3390/rs14195057
- Interannual and Seasonal Variability of Snow Depth Scaling Behavior in a Subalpine Catchment P. Mendoza et al. 10.1029/2020WR027343
- Review of snow water equivalent retrieval methods using spaceborne passive microwave radiometry N. Saberi et al. 10.1080/01431161.2019.1654144
- Snow Drought Risk and Susceptibility in the Western United States and Southwestern Canada J. Dierauer et al. 10.1029/2018WR023229
29 citations as recorded by crossref.
- Short-term streamflow time series prediction model by machine learning tool based on data preprocessing technique and swarm intelligence algorithm W. Niu et al. 10.1080/02626667.2020.1828889
- Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada F. King et al. 10.5194/hess-24-4887-2020
- Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction J. Chadalawada et al. 10.1029/2019WR026933
- Investigating ANN architectures and training to estimate snow water equivalent from snow depth K. Ntokas et al. 10.5194/hess-25-3017-2021
- Genetic programming for hydrological applications: to model or to forecast that is the question H. Herath et al. 10.2166/hydro.2021.179
- Data Assimilation for Streamflow Forecasting Using Extreme Learning Machines and Multilayer Perceptrons M. Boucher et al. 10.1029/2019WR026226
- A Statistical Analysis Model of Big Data for Precise Poverty Alleviation Based on Multisource Data Fusion T. Liang et al. 10.1155/2022/5298988
- Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry A. Horton et al. 10.3390/rs14246210
- Fusing daily snow water equivalent from 1980 to 2020 in China using a spatiotemporal XGBoost model L. Sun et al. 10.1016/j.jhydrol.2024.130876
- Development of a global operational snow analysis: The US Air Force Snow and Ice Analysis Y. Yoon et al. 10.1016/j.rse.2022.113080
- Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling H. Herath et al. 10.5194/hess-25-4373-2021
- Improving Snow Water Equivalent Maps With Machine Learning of Snow Survey and Lidar Measurements P. Broxton et al. 10.1029/2018WR024146
- Deep learning in environmental remote sensing: Achievements and challenges Q. Yuan et al. 10.1016/j.rse.2020.111716
- Exploring the Potential of Long Short‐Term Memory Networks for Improving Understanding of Continental‐ and Regional‐Scale Snowpack Dynamics Y. Wang et al. 10.1029/2021WR031033
- Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran H. Ghanjkhanlo et al. 10.1007/s11629-018-4875-8
- 3D radiative heating of tropical upper tropospheric cloud systems derived from synergistic A-Train observations and machine learning C. Stubenrauch et al. 10.5194/acp-21-1015-2021
- Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling S. Anderson & V. Radić 10.5194/hess-26-795-2022
- Projections of Snow Water Equivalent Using a Process-Based Energy Balance Snow Model in Southwestern British Columbia S. Sobie & T. Murdock 10.1175/JAMC-D-20-0260.1
- Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach D. Shao et al. 10.5194/essd-14-795-2022
- SNOTEL, the Soil Climate Analysis Network, and water supply forecasting at the Natural Resources Conservation Service: Past, present, and future S. Fleming et al. 10.1111/1752-1688.13104
- Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model A. Teweldebrhan et al. 10.5194/hess-24-4641-2020
- Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere Y. Hu et al. 10.3390/rs13071250
- A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning Y. Hu et al. 10.1080/20964471.2023.2177435
- Evaluation of Remote Sensing and Reanalysis Snow Depth Datasets over the Northern Hemisphere during 1980–2016 L. Xiao et al. 10.3390/rs12193253
- Snow water equivalent prediction in a mountainous area using hybrid bagging machine learning approaches K. Khosravi et al. 10.1007/s11600-022-00934-0
- Characteristics of Snow Depth and Snow Phenology in the High Latitudes and High Altitudes of the Northern Hemisphere from 1988 to 2018 S. Yue et al. 10.3390/rs14195057
- Interannual and Seasonal Variability of Snow Depth Scaling Behavior in a Subalpine Catchment P. Mendoza et al. 10.1029/2020WR027343
- Review of snow water equivalent retrieval methods using spaceborne passive microwave radiometry N. Saberi et al. 10.1080/01431161.2019.1654144
- Snow Drought Risk and Susceptibility in the Western United States and Southwestern Canada J. Dierauer et al. 10.1029/2018WR023229
Latest update: 19 Apr 2024
Short summary
Estimating winter snowpack throughout British Columbia is challenging due to the complex terrain, thick forests, and high snow accumulations present. This paper describes a way to make better snow estimates by combining publicly available data using machine learning, a branch of artificial intelligence research. These improved estimates will help water resources managers better plan for changes in rivers and lakes fed by spring snowmelt and will aid other research that supports such planning.
Estimating winter snowpack throughout British Columbia is challenging due to the complex...
