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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2019-593
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-593
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  08 Jan 2020

08 Jan 2020

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A revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

Application of machine learning techniques for regional bias correction of SWE estimates in Ontario, Canada

Fraser King1, Andre R. Erler2, Steven K. Frey2,3, and Christopher G. Fletcher1 Fraser King et al.
  • 1Dept. of Geography & Environmental Management, University of Waterloo, Ontario, Canada
  • 2Aquanty, Waterloo, Ontario, Canada
  • 3Dept. of Earth & Environmental Sciences, University of Waterloo, Ontario, Canada

Abstract. Snow is a critical contributor to Ontario's water-energy budget with impacts on water resource management and flood forecasting. Snow water equivalent (SWE) describes the amount of water stored in a snowpack and is important in deriving estimates of snowmelt. However, only a limited number of sparsely distributed snow survey sites (n = 383) exist throughout Ontario. The SNOw Data Assimilation System (SNODAS) is a daily, 1 km gridded SWE product that provides uniform spatial coverage across this region; however, we show here that SWE estimates from SNODAS display a strong positive mean bias of 50 % (16 mm SWE) when compared to in situ observations from 2011 to 2018. This study evaluates multiple statistical techniques of varying complexity, including simple subtraction, linear regression and machine learning methods to bias-correct SNODAS SWE estimates using absolute mean bias and RMSE as evaluation criteria. Results show that the random forest (RF) algorithm is most effective at reducing bias in SNODAS SWE, with an absolute mean bias of 0.2 mm and RMSE of 3.64 mm when compared with in situ observations. Other methods, such as mean bias subtraction and linear regression, are somewhat effective at bias reduction however, only the RF method captures the nonlinearity in the bias, and its interannual variability. Applying the RF model to the full spatio-temporal domain shows that the SWE bias is largest before 2015, during the spring melt period, north of 44.5° N and East (downwind) of the Great Lakes. As an independent validation, we also compare estimated snowmelt volumes with observed hydrographs, and demonstrate that uncorrected SNODAS SWE is associated with unrealistically large volumes at the time of the spring freshet, while bias-corrected SWE values are highly consistent with observed discharge volumes.

Fraser King et al.

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Fraser King et al.

Fraser King et al.

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Short summary
Snow is a critical contributor to our water and energy budget, with impacts to 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 are often considerable uncertainties associated in 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 to flooding and...
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