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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Publications at this Location » Publication #347799

Research Project: Uncertainty of Future Water Availability Due to Climate Change and Impacts on the Long Term Sustainability and Resilience of Agricultural Lands in the Southern Great Plains

Location: Agroclimate and Natural Resources Research

Title: Dynamic changes in snowfall extremes in the Songhua River Basin, northeastern China

Author
item ZHONG, KEYUAN - Northwest Agricultural & Forestry University
item ZHENG, FENLI - Northwest Agricultural & Forestry University
item Zhang, Xunchang
item QIN, CHAO - Northwest Agricultural & Forestry University
item XU, XIMENG - Northwest Agricultural & Forestry University
item LALIC, BRANISLAVA - University Of Novi Sad
item CUPINA, BRANKO - University Of Novi Sad

Submitted to: International Journal of Climatology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/27/2020
Publication Date: 5/4/2020
Citation: Zhong, K., Zheng, F., Zhang, X.J., Qin, C., Xu, X., Lalic, B., Cupina, B. 2020. Dynamic changes in snowfall extremes in the Songhua River Basin, northeastern China. International Journal of Climatology. https://doi.org/10.1002/joc.6628.
DOI: https://doi.org/10.1002/joc.6628

Interpretive Summary: Rainfall and snowfall differ greatly in terms of their effects on hydrological processes. Snowfall is usually regarded as snow water equivalent (SWE) in studying precipitation extremes, without considering the difference between snowfall and rainfall. Although snowfall is a key indicator of global change, no generally accepted, unified indices for the assessment of snowfall extremes currently exist. The objectives of this study are to identify a suite of snowfall indices that can be used to describe extreme snowfall events, to analyze the dynamic changes in the snowfall indices that have occurred in the Songhua River Basin (SRB), and to discuss possible correlations between the snowfall extremes and atmospheric circulation patterns. The study employs a dataset that contains daily data from 60 meteorological stations that cover a 55-year period. The results include a suite of snowfall indices that can be used to assess extreme snowfall events. These snowfall indices include four comprehensive indices, four intensity indices, four severity indices and two date indices. The total snowfall (SNTOT), the number of snowfall days (SND), the ratio of snowfall to total precipitation (S/P), the snowfall intensity (SNI), and the amounts of extreme snowfall (SN95TOT) and very extreme snowfall (SN99TOT) display insignificant trends over the entire SRB. The changes in the ending snowfall date (ESD) exhibit a significant advancing trend (p<0.001), while the changes in beginning snowfall date (BSD) display a significant delaying trend (p<0.05), which have led to a reduced snowfall season length (SSL) (P<0.001). The indices related to snowfall duration (SND and SSL) and snowfall date (BSD and ESD) show significant correlations with atmospheric circulation patterns. These results provide a series of reference indices with which changes in extreme snowfall events can be described, and they can enhance our understanding of the variations in snowfall that occur under global warming. This work would provide a useful tool for hydrologists to discriminate snowfall events from rainfall events and to improve hydrological prediction for rain-on-snow events.

Technical Abstract: Rainfall and snowfall differ greatly in terms of their effects on hydrological processes. Snowfall is usually regarded as snow water equivalent (SWE) in studying precipitation extremes, without considering the difference between snowfall and rainfall. Although snowfall is a key indicator of global change, no generally accepted, unified indices for the assessment of snowfall extremes currently exist. The objectives of this study are to identify a suite of snowfall indices that can be used to describe extreme snowfall events, to analyze the dynamic changes in the extreme snowfall indices that have occurred in the Songhua River Basin (SRB), and to discuss possible correlations between the snowfall extremes and atmospheric circulation patterns. The study employs a dataset that contains daily data from 60-meteorological stations that cover a 55-year period. The results include a suite of snowfall indices that can be used to assess extreme snowfall events. These snowfall indices include four comprehensive indices, four intensity indices, four severity indices and two date indices. The total snowfall (SNTOT), the number of snowfall days (SND), the ratio of snowfall to total precipitation (S/P), the snowfall intensity (SNI), and the amounts of extreme snowfall (SN95TOT) and very extreme snowfall (SN99TOT) display insignificant trends over the entire SRB. The changes in the ending snowfall date (ESD) exhibit a significant advancing trend (p<0.001), while the changes in beginning snowfall date (BSD) display a significant delaying trend (p<0.05), which have led to a reduced snowfall season length (SSL) (P<0.001). The indices related to snowfall duration (SND and SSL) and snowfall date (BSD and ESD) show significant correlations with atmospheric circulation patterns. These results provide a serie of reference indices with which changes in extreme snowfall events can be described, and they can enhance our understanding of the variations in snowfall that occur under global warming.