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

Abstract

. A h igh-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 20 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 25 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 R 2 of 0.81 (0.23), Root Mean 30 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 35 precision, with R 2 varying from 0.73 to 0.86. The fused snow depth had a decreasing trend 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 40 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. gridded datasets cross-validation fused snow depth dataset. snow pixel is complex, with grass, bare rock and forest. In a 0.25° pixel, this site only represents a small region; the elevation range was varied from 2700 m to 3900 m. These two sites can be used to observe the snowpack characteristics in a basin but can not represent the large area snow depth. The WFJ site is located on a hillside at an altitude of about 2540 m. The major landcover type is grassland in one pixel, but this site has a higher altitude. The elevation increased from 800 to 2600 m over 350 one pixel, so this site also has a complex environment. During the winter months, deeper snow builds up at this altitude. These results indicate that the accuracy of the fused snow depth dataset was heavily dependent on the input gridded snow depth products. Additionally, the snow depths changed too rapidly to be accurately captured by these products. In other words, the fused snow depth dataset had higher accuracy at the snow depths of less than 100 cm. Plateau had snow depth of about 5 to 10 cm. There were fewer in situ observations to train the random forest 410 data fusion framework, resulting in a lower snow depth accuracy in this region.