Articles | Volume 24, issue 10
https://doi.org/10.5194/hess-24-4887-2020
© Author(s) 2020. 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-24-4887-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Oct 2020
Research article |
| 14 Oct 2020
| 14 Oct 2020
Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada
Deptartment of Geography & Environmental Management, University of Waterloo, Ontario, Canada
Andre R. Erler
Aquanty, Waterloo, Ontario, Canada
Steven K. Frey
Aquanty, Waterloo, Ontario, Canada
Deptartment of Earth & Environmental Sciences, University of Waterloo, Ontario, Canada
Christopher G. Fletcher
Deptartment of Geography & Environmental Management, University of Waterloo, Ontario, Canada
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Cited
29 citations as recorded by crossref.
- Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region M. Notaro et al.
- Quantifying regional variability of machine-learning-based snow water equivalent estimates across the Western United States D. Liljestrand et al.
- Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning Z. Pei et al.
- Hydrological performance of ERA5 and MERRA-2 precipitation products over the Great Lakes Basin X. Xu et al.
- Identification and correction of snow depth bias in ERA5 datasets over Central Europe using machine learning G. Stachura & Z. Ustrnul
- Spatial patterns of snow distribution in the sub-Arctic K. Bennett et al.
- Big Data in Earth system science and progress towards a digital twin X. Li et al.
- Object-based ensemble estimation of snow depth and snow water equivalent over multiple months in Sodankylä, Finland D. Brodylo et al.
- Assessment of the Support Vector Regression and Random Forest Algorithms in the Bias Correction Process on Temperatures B. Miftahurrohmah et al.
- Impact of climate change on spatiotemporal patterns of snow hydrology: Conceptual frameworks, machine learning versus nested model M. Besharatifar & M. Nasseri
- Assessment of snow water equivalent characteristics in time and space over the Mackenzie River basin M. Soltani et al.
- A critical review of statistical, signal processing and machine learning methods for continuous and high-frequency water quality data improvement A. Badrudeen et al.
- Updated monthly and new daily bias correction for assimilation-based passive microwave SWE retrieval P. Venäläinen et al.
- Improving snow water equivalent modelling: a comparative study of hybrid machine learning techniques O. Pomarol Moya et al.
- UAV LiDAR surveys and machine learning improve snow depth and water equivalent estimates in boreal landscapes M. Ylönen et al.
- Spatial modeling of snow water equivalent in the high atlas mountains via a lumped process-based approach S. Acharki et al.
- Evaluating a hierarchy of bias correction methods for ERA5-Land SWE across Canada N. Kanda & C. Fletcher
- Enhancing precipitation intensity estimation using ERA5-land reanalysis with statistical and machine learning approaches A. Abdolmanafi et al.
- Evaluation of Correction Methods for ERA5 Shortwave Radiation Biases in China’s Second-Step Topographic Region: A Case Study of Hubei Province C. Xian et al.
- The importance of model horizontal resolution for improved estimation of snow water equivalent in a mountainous region of western Canada S. Sabetghadam et al.
- Retrieving water surface elevation over arctic thermokarst lakes using ICESat-2 and Sentinel-3 alti metry data S. Yekeen et al.
- Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques R. Anilkumar et al.
- GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers A. Umirbekov et al.
- Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance A. Beaton et al.
- Enhancing hydrological prediction in snow-dominant basins through multivariate calibration-assimilation framework F. Aderyani et al.
- On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow D. Tiwari et al.
- Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere Y. Hu et al.
- The effect of hydrological model structure on spring flow forecasts when assimilating a distributed snow product S. Farhoodi et al.
- SnowQM 1.0: a fast R package for bias-correcting spatial fields of snow water equivalent using quantile mapping A. Michel et al.
29 citations as recorded by crossref.
- Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region M. Notaro et al.
- Quantifying regional variability of machine-learning-based snow water equivalent estimates across the Western United States D. Liljestrand et al.
- Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning Z. Pei et al.
- Hydrological performance of ERA5 and MERRA-2 precipitation products over the Great Lakes Basin X. Xu et al.
- Identification and correction of snow depth bias in ERA5 datasets over Central Europe using machine learning G. Stachura & Z. Ustrnul
- Spatial patterns of snow distribution in the sub-Arctic K. Bennett et al.
- Big Data in Earth system science and progress towards a digital twin X. Li et al.
- Object-based ensemble estimation of snow depth and snow water equivalent over multiple months in Sodankylä, Finland D. Brodylo et al.
- Assessment of the Support Vector Regression and Random Forest Algorithms in the Bias Correction Process on Temperatures B. Miftahurrohmah et al.
- Impact of climate change on spatiotemporal patterns of snow hydrology: Conceptual frameworks, machine learning versus nested model M. Besharatifar & M. Nasseri
- Assessment of snow water equivalent characteristics in time and space over the Mackenzie River basin M. Soltani et al.
- A critical review of statistical, signal processing and machine learning methods for continuous and high-frequency water quality data improvement A. Badrudeen et al.
- Updated monthly and new daily bias correction for assimilation-based passive microwave SWE retrieval P. Venäläinen et al.
- Improving snow water equivalent modelling: a comparative study of hybrid machine learning techniques O. Pomarol Moya et al.
- UAV LiDAR surveys and machine learning improve snow depth and water equivalent estimates in boreal landscapes M. Ylönen et al.
- Spatial modeling of snow water equivalent in the high atlas mountains via a lumped process-based approach S. Acharki et al.
- Evaluating a hierarchy of bias correction methods for ERA5-Land SWE across Canada N. Kanda & C. Fletcher
- Enhancing precipitation intensity estimation using ERA5-land reanalysis with statistical and machine learning approaches A. Abdolmanafi et al.
- Evaluation of Correction Methods for ERA5 Shortwave Radiation Biases in China’s Second-Step Topographic Region: A Case Study of Hubei Province C. Xian et al.
- The importance of model horizontal resolution for improved estimation of snow water equivalent in a mountainous region of western Canada S. Sabetghadam et al.
- Retrieving water surface elevation over arctic thermokarst lakes using ICESat-2 and Sentinel-3 alti metry data S. Yekeen et al.
- Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques R. Anilkumar et al.
- GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers A. Umirbekov et al.
- Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance A. Beaton et al.
- Enhancing hydrological prediction in snow-dominant basins through multivariate calibration-assimilation framework F. Aderyani et al.
- On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow D. Tiwari et al.
- Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere Y. Hu et al.
- The effect of hydrological model structure on spring flow forecasts when assimilating a distributed snow product S. Farhoodi et al.
- SnowQM 1.0: a fast R package for bias-correcting spatial fields of snow water equivalent using quantile mapping A. Michel et al.
Saved (final revised paper)
Latest update: 30 Apr 2026
Short summary
Snow is a critical contributor to our water and energy budget, with impacts on flooding and water resource management. Measuring the amount of snow on the ground each year is an expensive and time-consuming task. Snow models and gridded products help to fill these gaps, yet there exist considerable uncertainties associated with their estimates. We demonstrate that machine learning techniques are able to reduce biases in these products to provide more realistic snow estimates across Ontario.
Snow is a critical contributor to our water and energy budget, with impacts on flooding and...
