Preprints
https://doi.org/10.5194/essd-2023-31
https://doi.org/10.5194/essd-2023-31
26 Jan 2023
 | 26 Jan 2023
Status: this preprint is currently under review for the journal ESSD.

NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series

Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, Adriaan J. Teuling, and Joshua R. Larsen

Abstract. Ground-based datasets of observed Snow Water Equivalent (SWE) are scarce, while gridded SWE estimates from remote-sensing and climate reanalysis are unable to resolve the high spatial variability of snow on the ground. Long-term ground observations of snow depth, in combination with models that can accurately convert snow depth to SWE, can fill this observational gap. Here, we provide a new SWE dataset (NH-SWE) that encompasses 11,071 stations in the Northern Hemisphere, and is available at https://doi.org/10.5281/zenodo.7515603 (Fontrodona-Bach et al., 2023). This new dataset provides daily time series of SWE, varying in length between one and seventy-three years, spanning the period 1950–2022 and covering a wide range of snow climates including many mountainous regions. At each station, observed snow depth was converted to SWE using an established snow-depth-to-SWE conversion model, with excellent model performance using regionalised parameters based on climate variables. The accuracy of the model after parameter regionalisation is comparable to that of the calibrated model. The key advantages and strengths of the regionalised model presented here are its transferability across climates and the high performance in modelling daily SWE dynamics in terms of peak SWE, total snowmelt and duration of the melt season, as assessed here against a comparison model. This dataset is particularly useful for studies that require accurate time series of SWE dynamics, timing of snowmelt onset, and snowmelt totals and duration. It can e.g. be used for climate change impact analyses, water resources assessment and management, validation of remote sensing of snow, hydrological modelling and snow data assimilation into climate models.

Adrià Fontrodona-Bach et al.

Status: open (until 21 Apr 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-31', Chunyu Dong, 24 Feb 2023 reply
    • AC1: 'Reply on RC1', Adrià Fontrodona-Bach, 28 Feb 2023 reply
  • CC1: 'Comment on essd-2023-31', Christoph Marty, 08 Mar 2023 reply
    • AC2: 'Reply on CC1', Adrià Fontrodona-Bach, 13 Mar 2023 reply
  • RC2: 'Comment on essd-2023-31', Anonymous Referee #2, 09 Mar 2023 reply
    • AC3: 'Reply on RC2', Adrià Fontrodona-Bach, 13 Mar 2023 reply

Adrià Fontrodona-Bach et al.

Data sets

NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series and the regionalisation of the ΔSNOW model Fontrodona-Bach, Adrià; Schaefli, Bettina; Woods, Ross; Teuling, Adriaan J.; Larsen, Joshua R. https://zenodo.org/record/7515603

Adrià Fontrodona-Bach et al.

Viewed

Total article views: 797 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
569 207 21 797 5 7
  • HTML: 569
  • PDF: 207
  • XML: 21
  • Total: 797
  • BibTeX: 5
  • EndNote: 7
Views and downloads (calculated since 26 Jan 2023)
Cumulative views and downloads (calculated since 26 Jan 2023)

Viewed (geographical distribution)

Total article views: 772 (including HTML, PDF, and XML) Thereof 772 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 30 Mar 2023
Download
Short summary
We provide a dataset of snow water equivalent, the depth of liquid water that results from melting a given depth of snow. The dataset contains 11,071 sites over the Northern Hemisphere, spans the period 1950–2022, and is based on daily observations of snow depth on the ground and a model. The dataset fills a lack of accessible historical ground snow data, and it can be used for a variety of applications such as the impact of climate change on global and regional snow and water resources.