Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-3017-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-3017-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Jun 2021
Research article |
| 04 Jun 2021
| 04 Jun 2021
Investigating ANN architectures and training to estimate snow water equivalent from snow depth
Konstantin F. F. Ntokas
CORRESPONDING AUTHOR
Department of Civil and Building Engineering, Université de Sherbrooke, Sherbrooke, Canada
Institute of Mathematics, Technische Universität Berlin, Berlin, Germany
Jean Odry
Department of Civil and Building Engineering, Université de Sherbrooke, Sherbrooke, Canada
Marie-Amélie Boucher
Department of Civil and Building Engineering, Université de Sherbrooke, Sherbrooke, Canada
Camille Garnaud
Environment and Climate Change Canada, Dorval, Canada
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Cited
27 citations as recorded by crossref.
- Development of ANN-Based Algorithm to Estimate Wintertime Sea Ice Temperature Profile Over the Arctic Ocean S. Baek et al. https://doi.org/10.1109/TGRS.2023.3293137
- Exploring the Potential of Long Short‐Term Memory Networks for Improving Understanding of Continental‐ and Regional‐Scale Snowpack Dynamics Y. Wang et al. https://doi.org/10.1029/2021WR031033
- Impacts of snow surface aerodynamic resistance on snow water equivalent simulations in forested regions X. Tang et al. https://doi.org/10.1002/hyp.14985
- Revisiting the Global Seasonal Snow Classification: An Updated Dataset for Earth System Applications M. Sturm & G. Liston https://doi.org/10.1175/JHM-D-21-0070.1
- Snowmelt Flood Susceptibility Assessment in Kunlun Mountains Based on the Swin Transformer Deep Learning Method R. Yang et al. https://doi.org/10.3390/rs14246360
- Development and application of a new simple algorithm for stochastic modeling of daily ground snow loads: A case study in Northeastern China H. Mo et al. https://doi.org/10.1016/j.coldregions.2025.104721
- Hybrid Data-Driven Models for Hydrological Simulation and Projection on the Catchment Scale S. Gharbia et al. https://doi.org/10.3390/su14074037
- Improved snow depth estimation on the Tibetan Plateau using AMSR2 and ensemble learning models Q. Gu et al. https://doi.org/10.1016/j.jag.2024.104102
- 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. https://doi.org/10.5194/essd-14-795-2022
- A stochastic approach to simulate realistic continuous snow depth time series J. Park & D. Kim https://doi.org/10.1016/j.jhydrol.2022.128980
- How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology D. Bilolikar et al. https://doi.org/10.2166/nh.2025.075
- Revealing causes of a surprising correlation: snow water equivalent and spatial statistics from Calibrated Enhanced-Resolution Brightness Temperatures (CETB) using interpretable machine learning and SHAP analysis M. Boueshagh et al. https://doi.org/10.3389/frsen.2025.1554084
- A hybrid NARX–LSTM neural network for predicting climate change impacts on snow cover area J. Hidalgo-Hidalgo et al. https://doi.org/10.1016/j.ejrh.2026.103478
- A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning Y. Hu et al. https://doi.org/10.1080/20964471.2023.2177435
- Estimation of Moderate-Resolution Snow Depth in Xinjiang With Enhanced-Resolution Passive Microwave and Reanalysis Data by Machine Learning Methods Y. Yan et al. https://doi.org/10.1109/JSTARS.2025.3562216
- Snow Depth Downscaling Retrieval Based on Spatial-Environment XGBoost Model: A Case Study of the Arid Region of Northwest China G. Wang et al. https://doi.org/10.1109/TGRS.2025.3597950
- NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in situ snow depth time series A. Fontrodona-Bach et al. https://doi.org/10.5194/essd-15-2577-2023
- PyGeoweaver: Tangible workflow tool for enhancing scientific research productivity and FAIRness G. Prathin et al. https://doi.org/10.1016/j.softx.2024.101863
- Moderate-resolution snow depth product retrieval from passive microwave brightness data over Xinjiang using machine learning approach Y. Liu et al. https://doi.org/10.1080/17538947.2023.2299208
- Combining Causal Inference with Machine Learning for Reconstructing Mountain Snow Water Equivalent Data Z. Ouyang et al. https://doi.org/10.3390/w18101243
- Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020) V. Vionnet et al. https://doi.org/10.5194/essd-13-4603-2021
- Improving snow water equivalent modelling: a comparative study of hybrid machine learning techniques O. Pomarol Moya et al. https://doi.org/10.5194/tc-20-1427-2026
- Integrating Physical Constraints Into Deep Learning for Enhanced Snow Depth Retrieval Over the Third Pole Y. Li et al. https://doi.org/10.1109/TGRS.2025.3598003
- Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al. https://doi.org/10.1016/j.jhydrol.2022.128835
- Estimating snow water equivalent using observed snow depth data in China Z. Yang et al. https://doi.org/10.1016/j.ejrh.2024.101664
- Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks L. Böhm et al. https://doi.org/10.3390/en16186643
- Object-based ensemble estimation of snow depth and snow water equivalent over multiple months in Sodankylä, Finland D. Brodylo et al. https://doi.org/10.5194/tc-19-6127-2025
27 citations as recorded by crossref.
- Development of ANN-Based Algorithm to Estimate Wintertime Sea Ice Temperature Profile Over the Arctic Ocean S. Baek et al. https://doi.org/10.1109/TGRS.2023.3293137
- Exploring the Potential of Long Short‐Term Memory Networks for Improving Understanding of Continental‐ and Regional‐Scale Snowpack Dynamics Y. Wang et al. https://doi.org/10.1029/2021WR031033
- Impacts of snow surface aerodynamic resistance on snow water equivalent simulations in forested regions X. Tang et al. https://doi.org/10.1002/hyp.14985
- Revisiting the Global Seasonal Snow Classification: An Updated Dataset for Earth System Applications M. Sturm & G. Liston https://doi.org/10.1175/JHM-D-21-0070.1
- Snowmelt Flood Susceptibility Assessment in Kunlun Mountains Based on the Swin Transformer Deep Learning Method R. Yang et al. https://doi.org/10.3390/rs14246360
- Development and application of a new simple algorithm for stochastic modeling of daily ground snow loads: A case study in Northeastern China H. Mo et al. https://doi.org/10.1016/j.coldregions.2025.104721
- Hybrid Data-Driven Models for Hydrological Simulation and Projection on the Catchment Scale S. Gharbia et al. https://doi.org/10.3390/su14074037
- Improved snow depth estimation on the Tibetan Plateau using AMSR2 and ensemble learning models Q. Gu et al. https://doi.org/10.1016/j.jag.2024.104102
- 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. https://doi.org/10.5194/essd-14-795-2022
- A stochastic approach to simulate realistic continuous snow depth time series J. Park & D. Kim https://doi.org/10.1016/j.jhydrol.2022.128980
- How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology D. Bilolikar et al. https://doi.org/10.2166/nh.2025.075
- Revealing causes of a surprising correlation: snow water equivalent and spatial statistics from Calibrated Enhanced-Resolution Brightness Temperatures (CETB) using interpretable machine learning and SHAP analysis M. Boueshagh et al. https://doi.org/10.3389/frsen.2025.1554084
- A hybrid NARX–LSTM neural network for predicting climate change impacts on snow cover area J. Hidalgo-Hidalgo et al. https://doi.org/10.1016/j.ejrh.2026.103478
- A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning Y. Hu et al. https://doi.org/10.1080/20964471.2023.2177435
- Estimation of Moderate-Resolution Snow Depth in Xinjiang With Enhanced-Resolution Passive Microwave and Reanalysis Data by Machine Learning Methods Y. Yan et al. https://doi.org/10.1109/JSTARS.2025.3562216
- Snow Depth Downscaling Retrieval Based on Spatial-Environment XGBoost Model: A Case Study of the Arid Region of Northwest China G. Wang et al. https://doi.org/10.1109/TGRS.2025.3597950
- NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in situ snow depth time series A. Fontrodona-Bach et al. https://doi.org/10.5194/essd-15-2577-2023
- PyGeoweaver: Tangible workflow tool for enhancing scientific research productivity and FAIRness G. Prathin et al. https://doi.org/10.1016/j.softx.2024.101863
- Moderate-resolution snow depth product retrieval from passive microwave brightness data over Xinjiang using machine learning approach Y. Liu et al. https://doi.org/10.1080/17538947.2023.2299208
- Combining Causal Inference with Machine Learning for Reconstructing Mountain Snow Water Equivalent Data Z. Ouyang et al. https://doi.org/10.3390/w18101243
- Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020) V. Vionnet et al. https://doi.org/10.5194/essd-13-4603-2021
- Improving snow water equivalent modelling: a comparative study of hybrid machine learning techniques O. Pomarol Moya et al. https://doi.org/10.5194/tc-20-1427-2026
- Integrating Physical Constraints Into Deep Learning for Enhanced Snow Depth Retrieval Over the Third Pole Y. Li et al. https://doi.org/10.1109/TGRS.2025.3598003
- Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al. https://doi.org/10.1016/j.jhydrol.2022.128835
- Estimating snow water equivalent using observed snow depth data in China Z. Yang et al. https://doi.org/10.1016/j.ejrh.2024.101664
- Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks L. Böhm et al. https://doi.org/10.3390/en16186643
- Object-based ensemble estimation of snow depth and snow water equivalent over multiple months in Sodankylä, Finland D. Brodylo et al. https://doi.org/10.5194/tc-19-6127-2025
Saved (final revised paper)
Latest update: 11 Sep 2026
Short summary
This article shows a conversion model of snow depth into snow water equivalent (SWE) using an ensemble of artificial neural networks. The novelty is a direct estimation of SWE and the improvement of the estimation by in-depth analysis of network structures. The usage of an ensemble allows a probabilistic estimation and, therefore, a deeper insight. It is a follow-up study of a similar study over Quebec but extends it to the whole area of Canada and improves it further.
This article shows a conversion model of snow depth into snow water equivalent (SWE) using an...
