Analysis of the Variability and Future Evolution of Snowfall Trends in the Huaihe River Basin Under Climate Change

• Main Authors: Hengxin Dong, Qiangyu Li, Xiaochen Zhu, Xinyu Zhang, Zilu Zhang, Jian Shi, Yongjian He
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-10-01
• Series: Frontiers in Earth Science
• Subjects: climate warming; snowfall; trend change; analysis of abrupt changes; climate change scenario
• Online Access: https://www.frontiersin.org/articles/10.3389/feart.2020.594704/full

Global warming changes the characteristics of regional climate and crop growth environments and affects human production and life in the long term. In this study, historical snowfall observation data from 199 conventional meteorological stations in the Huaihe River Basin were analyzed with the climate trend method, the nonparametric Mann-Kendall (M-K) test and the sliding t test to determine the variability and abrupt changes in climate trends in the Huaihe River Basin during 1951–2018. Then, the Coupled Model Intercomparison Project phase 6 datasets were used to analyze the evolutionary trend of snowfall under four climate change scenarios in 2015–2065. The results show the following: 1) Due to climate warming, both the historical data and the simulated data under different climate change scenarios show declining snowfall in the Huaihe River Basin. 2) Based on the analysis of historical data from meteorological stations, the snow events had a clear latitudinal distribution—the higher the latitude, the lower the occurrence of snow events. In contrast, as shown by the analysis results obtained under different climate change scenarios, the snow mass flux had a clear longitudinal distribution—the higher the longitude, the higher the snow mass flux. 3) In Scenario SSP370, 2050 is important because the snowfall changes from decreasing to increasing, and in 2065, the snowfall in most areas of the basin is greater than that in 2050. Furthermore, the snowfall along the southeastern coast of the basin has the greatest variability.