Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)

Vincent Vionnet,Colleen Mortimer,Louise Arnal,Mike Brady,Ross Brown

doi:10.5194/essd-13-4603-2021

Abstract

Abstract. In situ measurements of water equivalent of snow cover (SWE) – the vertical depth of water that would be obtained if all the snow cover melted completely – are used in many applications including water management, flood forecasting, climate monitoring, and evaluation of hydrological and land surface models. The Canadian historical SWE dataset (CanSWE) combines manual and automated pan-Canadian SWE observations collected by national, provincial and territorial agencies as well as hydropower companies. Snow depth (SD) and bulk snow density (defined as the ratio of SWE to SD) are also included when available. This new dataset supersedes the previous Canadian Historical Snow Survey (CHSSD) dataset published by Brown et al. (2019), and this paper describes the efforts made to correct metadata, remove duplicate observations and quality control records. The CanSWE dataset was compiled from 15 different sources and includes SWE information for all provinces and territories that measure SWE. Data were updated to July 2020, and new historical data from the Government of Northwest Territories, Government of Newfoundland and Labrador, Saskatchewan Water Security Agency, and Hydro-Québec were included. CanSWE includes over 1 million SWE measurements from 2607 different locations across Canada over the period 1928–2020. It is publicly available at https://doi.org/10.5281/zenodo.4734371 (Vionnet et al., 2021).

Highlights

IntroductionReliable in situ information of snow water equivalent (SWE) or more precisely water equivalent of snow cover according to WMO (2018) – the vertical depth of water that would be obtained if the snow cover melted completely, which equates to the snow-cover mass per unit area (WMO, 2018) – is critical for flood and drought predictions (e.g., Jörg-Hess et al, 2015; Berghuijs et al, 2016; Vionnet et al, 2020), streamflow management of water supply for hydropower generation (e.g., Magnusson et al, 2020), and irrigation planning (e.g., Biemans et al, 2019) and is a key environmental variable for climate monitoring and understanding (e.g., Clark et al, 2001; Brown et al, 2019)
Snow depth (SD) and bulk snow density are included when available. This new dataset supersedes the previous Canadian Historical Snow Survey (CHSSD) dataset published by Brown et al (2019), and this paper describes the efforts made to correct metadata, remove duplicate observations and quality control records
Reliable in situ information of snow water equivalent (SWE) or more precisely water equivalent of snow cover according to WMO (2018) – the vertical depth of water that would be obtained if the snow cover melted completely, which equates to the snow-cover mass per unit area (WMO, 2018) – is critical for flood and drought predictions (e.g., Jörg-Hess et al, 2015; Berghuijs et al, 2016; Vionnet et al, 2020), streamflow management of water supply for hydropower generation (e.g., Magnusson et al, 2020), and irrigation planning (e.g., Biemans et al, 2019) and is a key environmental variable for climate monitoring and understanding (e.g., Clark et al, 2001; Brown et al, 2019)

Summary

Introduction

Reliable in situ information of snow water equivalent (SWE) or more precisely water equivalent of snow cover according to WMO (2018) – the vertical depth of water that would be obtained if the snow cover melted completely, which equates to the snow-cover mass per unit area (WMO, 2018) – is critical for flood and drought predictions (e.g., Jörg-Hess et al, 2015; Berghuijs et al, 2016; Vionnet et al, 2020), streamflow management of water supply for hydropower generation (e.g., Magnusson et al, 2020), and irrigation planning (e.g., Biemans et al, 2019) and is a key environmental variable for climate monitoring and understanding (e.g., Clark et al, 2001; Brown et al, 2019). In situ SWE measurements can be made manually or via automatic sensors (Kinar and Pomeroy, 2015). Manual snow surveys are generally representative of the prevailing land cover and terrain but are time-consuming and expensive, which limits their temporal frequency, especially in remote locations. Automatic stations can overcome this limitation and provide SWE measurements at a higher temporal frequency but have the disadvantage of only measuring SWE at a single point. Snow pillows (Beaumont, 1965) and snow scales (Johnson, 2004; Smith et al, 2017) automatically measure SWE from the overlying pressure and weight of the snowpack, respectively. Indirect methods using passive radiation sensors installed below or above the snowpack have been developed. They measure the attenuation by the snowpack of nat-

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