Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach

Abstract

Abstract. The snow water equivalent (SWE) is an important parameter of surface hydrological and climate systems, and it has a profound impact on Arctic amplification and climate change. However, there are great differences among existing SWE products. In the land region above 45∘ N, the existing SWE products are associated with a limited time span and limited spatial coverage, and the spatial resolution is coarse, which greatly limits the application of SWE 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 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 SWE products on a global scale. We evaluated the accuracy of the RRM SWE product using hemispheric-scale snow course (HSSC) observational data and Russian snow survey data. The mean absolute error (MAE), RMSE, R, and R2 between the RRM SWE products and observed SWEs are 0.21, 25.37 mm, 0.89, and 0.79, respectively. The accuracy of the RRM SWE dataset is improved by 28 %, 22 %, 37 %, 11 %, and 11 % compared with the original AMSR-E/AMSR2 (SWE), ERA-Interim SWE, Global Land Data Assimilation System (GLDAS) SWE, GlobSnow SWE, and ERA5-Land SWE datasets, respectively, and it has a higher spatial resolution. The RRM SWE product production method does not rely heavily on an independent SWE product; it takes full advantage of each SWE dataset, and it takes into consideration the altitude factor. The MAE ranges from 0.16 for areas within <100 m elevation to 0.29 within the 800–900 m elevation range. The MAE is best in the Russian region and worst in the Canadian region. The RMSE ranges from 4.71 mm for areas within <100 m elevation to 31.14 mm within the >1000 m elevation range. The RMSE is best in the Finland region and worst in the Canadian region. This method has good stability, is extremely suitable for the production of snow datasets with large spatial scales, and can be easily extended to the preparation of other snow datasets. The RRM SWE product is expected to provide more accurate SWE 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 “A Big Earth Data Platform for Three Poles” (https://doi.org/10.11888/Snow.tpdc.271556) (Li et al., 2021).

Rising temperatures drive lower summer minimum flows across hydrologically diverse catchments

“…We assess below-normal winter snow storage using ERA5-Land reanalysis snow water equivalent (SWE) data . Shao et al (2022) found that these data performed better than other published gridded snow data products, and we also found that this dataset provided a much better match to snow survey data (Vionnet et al, 2021), than a Canadian data product (Environment and Climate Change Canada, 2021). 𝑆𝑊𝐸 𝑚𝑎𝑥 is the maximum catchment-averaged SWE for each year.…”

Recent Increase in the Occurrence of Snow Droughts Followed by Extreme Heatwaves in a Warmer World

“…In this study, we used the state-of-the-art ERA5-Land data set, which is featured with flexible spatial and temporal resolution (Muñoz-Sabater et al, 2021) and good performance in identifying extreme temperature events (Sheridan et al, 2020). More importantly, the SWE of ERA5-Land agrees better with station observations compared with other datasets (Shao et al, 2022), making it an ideal data set to characterize snow droughts. To further demonstrate the robustness of our results, we compared the CSDHW events detected based on ERA5-Land with those by chance (You & Wang, 2021).…”

Northern Hemisphere Snow Drought in Earth System Model Simulations and ERA5‐Land Data in 1980–2014

“…ERA5‐Land generally outperforms ERA5 over the US, while ERA5 performs better over Europe. It is noteworthy that the ERA5‐Land SWE has good performance for heights ranging between ∼1,500 and ∼3,000 m (Muñoz‐Sabater et al., 2021) and the best performance in the elevation interval of 300–400 m (Shao et al., 2022) but it is also known to have too large SWE sum estimates over the mountainous regions.…”

“…To evaluate our model results, we use the latest land component of the reanalysis product ERA5-Land produced by the European Center for Medium-Range Weather Forecasts as a reference. This product is an enhanced global data set for ERA5 (Muñoz-Sabater, 2019; Muñoz-Sabater et al, 2021), which has been found to perform well in the evaluation of nine gridded Northern Hemisphere snow SWE products as part of the European Space Agency Satellite Snow Product Intercomparison and Evaluation Exercise (SnowPEx) across a range of snow conditions (Mortimer et al, 2020;Shao et al, 2022). Although Mortimer et al (2020) did not explicitly endorse any single product, they did note that ERA5 is one of the products that perform best across the range of snow conditions captured by the validation data set.…”