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
16 citations as recorded by crossref.
- Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region M. Notaro et al. 10.1175/JHM-D-21-0025.1
- Quantifying regional variability of machine-learning-based snow water equivalent estimates across the Western United States D. Liljestrand et al. 10.1016/j.envsoft.2024.106053
- Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning Z. Pei et al. 10.1016/j.corsci.2020.108697
- Hydrological performance of ERA5 and MERRA-2 precipitation products over the Great Lakes Basin X. Xu et al. 10.1016/j.ejrh.2021.100982
- Spatial patterns of snow distribution in the sub-Arctic K. Bennett et al. 10.5194/tc-16-3269-2022
- Big Data in Earth system science and progress towards a digital twin X. Li et al. 10.1038/s43017-023-00409-w
- Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques R. Anilkumar et al. 10.5194/tc-17-2811-2023
- 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. 10.5194/gmd-17-911-2024
- Assessment of the Support Vector Regression and Random Forest Algorithms in the Bias Correction Process on Temperatures B. Miftahurrohmah et al. 10.1016/j.procs.2024.03.049
- Impact of climate change on spatiotemporal patterns of snow hydrology: Conceptual frameworks, machine learning versus nested model M. Besharatifar & M. Nasseri 10.1016/j.pce.2024.103691
- 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. 10.1002/hyp.15075
- On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow D. Tiwari et al. 10.5194/hess-28-1127-2024
- Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere Y. Hu et al. 10.3390/rs13071250
- The effect of hydrological model structure on spring flow forecasts when assimilating a distributed snow product S. Farhoodi et al. 10.1080/07011784.2024.2434517
- SnowQM 1.0: a fast R package for bias-correcting spatial fields of snow water equivalent using quantile mapping A. Michel et al. 10.5194/gmd-17-8969-2024
- Bias correction method of high-resolution satellite-based precipitation product for Peninsular Malaysia Z. Iqbal et al. 10.1007/s00704-022-04007-6
15 citations as recorded by crossref.
- Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region M. Notaro et al. 10.1175/JHM-D-21-0025.1
- Quantifying regional variability of machine-learning-based snow water equivalent estimates across the Western United States D. Liljestrand et al. 10.1016/j.envsoft.2024.106053
- Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning Z. Pei et al. 10.1016/j.corsci.2020.108697
- Hydrological performance of ERA5 and MERRA-2 precipitation products over the Great Lakes Basin X. Xu et al. 10.1016/j.ejrh.2021.100982
- Spatial patterns of snow distribution in the sub-Arctic K. Bennett et al. 10.5194/tc-16-3269-2022
- Big Data in Earth system science and progress towards a digital twin X. Li et al. 10.1038/s43017-023-00409-w
- Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques R. Anilkumar et al. 10.5194/tc-17-2811-2023
- 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. 10.5194/gmd-17-911-2024
- Assessment of the Support Vector Regression and Random Forest Algorithms in the Bias Correction Process on Temperatures B. Miftahurrohmah et al. 10.1016/j.procs.2024.03.049
- Impact of climate change on spatiotemporal patterns of snow hydrology: Conceptual frameworks, machine learning versus nested model M. Besharatifar & M. Nasseri 10.1016/j.pce.2024.103691
- 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. 10.1002/hyp.15075
- On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow D. Tiwari et al. 10.5194/hess-28-1127-2024
- Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere Y. Hu et al. 10.3390/rs13071250
- The effect of hydrological model structure on spring flow forecasts when assimilating a distributed snow product S. Farhoodi et al. 10.1080/07011784.2024.2434517
- SnowQM 1.0: a fast R package for bias-correcting spatial fields of snow water equivalent using quantile mapping A. Michel et al. 10.5194/gmd-17-8969-2024
1 citations as recorded by crossref.
Latest update: 24 Dec 2024
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...
