Abstract

Snow water equivalent is an important parameter of the surface hydrological and climate systems, and it has a profound impact on Arctic amplification and climate change. However, there are great differences among existing snow water equivalent products. In the Pan-Arctic region, the existing snow water equivalent products are limited time span and limited spatial coverage, and the spatial resolution is coarse, which greatly limits the application of snow water equivalent data in cryosphere change and climate change studies. In this study, utilizing the ridge regression model (RRM) of a machine learning algorithm, we integrated various existing snow water equivalent (SWE) products to generate a spatiotemporally seamless and high-precision RRM SWE product. The results show that it is feasible to utilize a ridge regression model based on a machine learning algorithm to prepare snow water equivalent products on a global scale. We evaluated the accuracy of the RRM SWE product using Global Historical Climatology Network (GHCN) data and Russian snow survey data. The MAE, RMSE, R, and R2; between the RRM SWE products and observed snow water equivalents are 0.24, 30.29 mm, 0.87, and 0.76, respectively. The accuracy of the RRM SWE dataset is improved by 24 %, 25 %, 32 %, 7 %, and 10 % compared with the original AMSR-E/AMSR2 snow water equivalent dataset, ERA-Interim SWE dataset, Global Land Data Assimilation System (GLDAS) SWE dataset, GlobSnow SWE dataset, and ERA5-land SWE dataset, respectively, and it has a higher spatial resolution. The RRM SWE product production method does not rely too much on an independent snow water equivalent product, it makes full use of the advantages of each snow water equivalent dataset, and it considers the altitude factor. The average MAE of RRM SWE product at different altitude intervals is 0.24 and the average RMSE is 23.55 mm, this method has good stability, it is extremely suitable for the production of snow datasets with large spatial scales, and it can be easily extended to the preparation of other snow datasets. The RRM SWE product is expected to provide more accurate snow water equivalent data for the hydrological model and climate model and provide data support for cryosphere change and climate change studies. The RRM SWE product is available from the ‘A Big Earth Data Platform for Three Poles’ (http://dx.doi.org/10.11888/Snow.tpdc.271556) (Li et al., 2021).

Highlights

  • The accuracy of the regression model (RRM) SWE dataset is improved by 24%, 25%, 32%, 7%, and 10% compared with the original Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E)/AMSR2 snow water equivalent dataset, ERA-Interim SWE dataset, Global Land Data Assimilation System (GLDAS) SWE

  • The snow water equivalent data observed at stations from 1979 to 2014 are used as sample training data, and the AMSR-E/AMSR2 SWE, ERA-Interim SWE, GLDAS SWE, GlobSnow SWE, ERA5-land SWE data, and digital elevation model (DEM) data are input into the ridge regression model of a machine learning algorithm for training

  • To further evaluate the accuracy of the RRM SWE dataset at the spatial scale, we compared it with AMSR-E/AMSR2 SWE, ERA-Interim SWE, GLDAS SWE, GlobSnow SWE, and ERA5-Land SWE at different altitude gradients

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Summary

Introduction

The IPCC (Intergovernmental Panel on Climate Change) AR6 (Sixth Assessment Report) notes that the Northern Hemisphere spring snow cover has greatly reduced since 1950, and the feedback effect of the climate system caused by this reduction is extremely large (Masson-Delmotte et al, 2021). The snow water equivalent data from stations and meteorological observations cannot meet the needs of hydrometeorological and climate change research This is mainly because SWE from stations is discontinuous in time series and severely missing. Machine learning methods can integrate the advantages of multisource data and make full use of site observation data to train the sample data, which generates snow water equivalent data products with large spatial scales and long time series (Broxton et al, 2019; Bair et al, 2018). We integrated multisource snow water equivalent data products of RRM SWE based on the ridge regression model of the machine learning algorithm. The spatial coverage of the RRM SWE product covers all land regions north of 45° N

Research region
Grid snow water equivalent data description
Ridge regression machine learning algorithm for preparing snow water equivalent
Site snow water equivalent data for training, validation, and testing
Accuracy evaluation method for datasets
Overall accuracy evaluation of the RRM SWE product
Accuracy evaluation of the RRM SWE product at different altitudes
Comparison of spatial distribution patterns between the RRM SWE product and traditional snow water equivalent
Conclusions
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