Reconstruction of Snow Depth Data at Moderate Spatial Resolution (1 km) from Remotely Sensed Snow Data and Multiple Optimized Environmental Factors: A Case Study over the Qinghai-Tibetan Plateau

An Approach to Improve the Spatial Resolution and Accuracy of AMSR2 Passive Microwave Snow Depth Product Using Machine Learning in Northeast China

Snow cover plays a highly critical role in the global water cycle and energy exchange. Accurate snow depth (SD) data are important for research on hydrologic processes, climate change, and natural disaster prediction. However, existing passive microwave (PMW) SD products have high uncertainty in Northeast China owing to their coarse spatial resolution. Surface environment parameters should also be considered to reduce errors in existing SD products. Otherwise, it is difficult to accurately capture snow spatiotemporal variations, especially in a complex environment (e.g., mountain or forests areas). To improve the inversion accuracy and spatial resolution of existing SD products in Northeast China, a multifactor SD downscaling model was developed by combining PMW SD data from the AMSR2 sensor, optical snow cover extent data, and surface environmental parameters to produce fine scale (500 m × 500 m) and high precision SD data. Validations at 98 ground meteorological stations show that the developed model greatly improved the spatial resolution and inversion accuracy of the raw AMSR2 SD product; its root-mean-square error (RMSE) reduced from 26.15 cm of the raw product to 7.58 cm, and the correlation coefficient (R) increased from 0.39 to 0.53. For other SD products (WESTDC and FY), the multifactor SD downscaling model still has good applicability, it could further improve the performance of the WESTDC and FY SD products in time and space and achieve better inversion accuracy than raw SD products. Furthermore, the proposed model exhibited good agreement with the observed SD data in a field quadrat (3 km × 2 km) within the fine scale, with an error ranging between −2 and 2 cm. Compared with the existing downscaling methods, the proposed model presented the best performance.

Changes in climate and snow cover and their synergistic influence on spring runoff in the source region of the Yellow River

As an important runoff-producing area, the runoff variation in the source region of the Yellow River (SRYR) has critical importance for the whole basin in broad aspects. In recent decades, the climate in the SRYR has undergone drastic changes, which affected runoff across different time scales. Many studies have focused on runoff in the SRYR with a long-time series, and presented a discordant relationship between precipitation and runoff. However, differences in this relationship over different time scales are ignored. Here, by using multi-source observation data and correlation analysis, climate elasticity, and principal component analysis methods, we document the changes in climate and snow cover and their synergistic influence on spring runoff. When the 20-year period was innovatively adopted, the runoff and precipitation coincided well during last three periods (1960-2019). The yearly precipitation presented a bimodal pattern, with the most significant increase in late spring and early summer. A bimodal pattern also appeared in annual runoff, and the rate of increase was much greater than that of precipitation (2.51%/year vs 1.01%/year). The runoff during main increase period (particularly in April) showed a high correlation with the remote sensing snow depth from November to March, but a poor correlation with snow depth from meteorological stations. Climate warming in the SRYR was much more reflected in minimum surface temperature (0.235 °C/year) than in air temperature minimum (0.081 °C/year) in last 20 years. However, the principal component analysis shows that the effect of temperature on spring runoff was obviously less than that of snow cover. A 1% variation in snow depth in the SRYR from November to March caused a 0.43% variation in runoff in April, and a 1% variation in snow days caused a 0.82% variation in runoff. This study will bring to light for understanding the evolution mechanism of spring runoff in the SRYR.