Preprints
https://doi.org/10.5194/essd-2022-63
https://doi.org/10.5194/essd-2022-63
28 Mar 2022
 | 28 Mar 2022
Status: this preprint has been withdrawn by the authors.

A long-term daily gridded snow depth dataset for the Northern Hemisphere from 1980 to 2019 based on machine learning

Yanxing Hu, Tao Che, Liyun Dai, Yu Zhu, Lin Xiao, Jie Deng, and Xin Li

Abstract. A high-quality snow depth product is very import for cryospheric science and its related disciplines. Current long time-series snow depth products covering the Northern Hemisphere can be divided into two categories: remote sensing snow depth product and reanalysis snow depth products. However, existing gridded snow depth products have some shortcomings. Remote sensing-derived snow depth products are temporally and spatially discontinuous and tend to underestimate snow depth, while reanalysis snow depth products have coarse spatial resolutions and great uncertainties. To overcome these problems, in our previous work we proposed a novel data fusion framework based on Random Forest Regression of snow products from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR-2), Global Snow Monitoring for Climate Research (GlobSnow), the Northern Hemisphere Snow Depth (NHSD), ERA-Interim, and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), incorporated geolocation (latitude and longitude), and topographic data (elevation), which were used as input independent variables. More than 30,000 ground observation sites were used as the dependent variable to train and validate the model in different time period. This fusion framework resulted in a long time series of continuous daily snow depth product over the Northern Hemisphere with a spatial resolution of 0.25°. Here we compared the fused snow depth and the original gridded snow depth products with 13,272 observation sites, showing an improved precision of our product. The evaluation indexes of the fused (best original) dataset yielded a coefficient of determination R2 of 0.81 (0.23), Root Mean Squared Error (RMSE) of 7.69 (15.86) cm, and Mean Absolute Error (MAE) of 2.74 (6.14) cm. Most of the bias (88.31 %) between the fused snow depth and in situ observations was distributed from -5 cm to 5 cm depths. The accuracy assessment of independent snow observation sites – Sodankylä (SOD), Old Aspen (OAS), Old Black Spruce (OBS), and Old Jack Pine (OJP) – showed that the fused snow depth dataset had high precision under snow depths of less than 100 cm with a relatively homogeneous surrounding environment. In the altitude range of 100 m to 2000 m, the fused snow depth had a higher precision, with R2 varying from 0.73 to 0.86. The fused snow depth had consistent trends based on the spatiotemporal analysis and Mann-Kendall trend test method. This fused snow depth product provides the basis for understanding the temporal and spatial characteristics of snow cover and their relation to climate change, hydrological and water cycle, water resource management, ecological environment and snow disaster and hazard prevention. The new fused snow depth dataset is freely available from the National Plateau Data Center (TPDC) and can be downloaded at https://dx.doi.org/10.11888/Snow.tpdc.271701 (Che et al., 2021). This snow depth also can be downloaded at  https://zenodo.org/record/6336866#.Yjs0CMjjwzY.

This preprint has been withdrawn.

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Yanxing Hu, Tao Che, Liyun Dai, Yu Zhu, Lin Xiao, Jie Deng, and Xin Li

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-63', Baptiste Vandecrux, 03 May 2022
  • RC2: 'Comment on essd-2022-63', Anonymous Referee #2, 24 Jun 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-63', Baptiste Vandecrux, 03 May 2022
  • RC2: 'Comment on essd-2022-63', Anonymous Referee #2, 24 Jun 2022
Yanxing Hu, Tao Che, Liyun Dai, Yu Zhu, Lin Xiao, Jie Deng, and Xin Li

Data sets

Long-term series of daily snow depth dataset over the Northern Hemisphere based on machine learning (1980-2019) Che, T., Hu, Y., Dai, L., Xiao, L. https://zenodo.org/record/6336866#.Yjs0CMjjwzY

Long-term series of daily snow depth dataset over the Northern Hemisphere based on machine learning (1980-2019) Che, T., Hu, Y., Dai, L., Xiao, L. https://dx.doi.org/10.11888/Snow.tpdc.271701

Yanxing Hu, Tao Che, Liyun Dai, Yu Zhu, Lin Xiao, Jie Deng, and Xin Li

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Latest update: 23 Jun 2024
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Short summary
We propose a data fusion framework based on the random forest regression algorithm to derive a comprehensive snow depth product for the Northern Hemisphere from 1980 to 2019. This new fused snow depth dataset not only provides information about snow depth and its variation over the Northern Hemisphere but also presents potential value for hydrological and water cycle studies related to seasonal snowpacks.
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