References

NHESSD

Natural Hazards and Earth System Sciences Discussions

NHESSD

Nat. Hazards Earth Syst. Sci. Discuss.

2195-9269

Göttingen, Germany

10.5194/nhessd-1-7059-2013

Temporal and spatial variability of extreme snowfall indices over northern Xinjiang from 1959/1960 to 2008/2009

Wang

S. P.

¹ ² Jiang

F. Q.

¹ Hu

R. J.

¹ Zhang

Y. W.

Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China

University of Chinese Academy of Sciences, Beijing 100049, China

Department of Environment and Planning, Shangqiu Normal University, Shangqiu, Henan 476000, China

05 12 2013

1 6 7059 7092

2013

This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/

This article is available from https://nhess.copernicus.org/preprints/1/7059/2013/nhessd-1-7059-2013.html

The full text article is available as a PDF file from https://nhess.copernicus.org/preprints/1/7059/2013/nhessd-1-7059-2013.pdf

Plentiful snowfall is an important resource in northern Xinjiang. However, extreme snowfall events can lead to destructive avalanches, traffic interruptions or even the collapse of buildings. The daily winter precipitation data from 18 stations in northern Xinjiang during 1959/1960–2008/2009 were selected for purpose of analyzing long-term variability of extreme snowfall events. Five extreme snowfall indices, Maximum 1 day snowfall amount (SX1day), Maximum 1-weather process snowfall amount (SX1process), Blizzard days (DSb), Consecutive snow days (DSc) and Blizzard weather processes (PSb), were defined and utilized to quantitatively describe the intensity and frequency of extreme snowfall events. Temporal trends of the five indices were analyzed by Mann–Kendall test and simple linear regression, and their trends were interpolated using universal kriging interpolation. Temporally, we found that most stations have upward trends in the five indices of extreme snowfall events, and over entire northern Xinjiang, they were all increasing at the 0.01 significance level (MK test), with the linear tendency rates of 0.49 mm (10 a)−1 (SX1day), 0.89 mm (10 a)−1 (SX1process), 0.024 days (10 a)−1 (DSb), 0.14 days (10 a)−1 (DSc), and 0.069 times (10 a)−1 (PSb) respectively. Meanwhile, obvious decadal fluctuations besides long-term increasing trends are identified. Trends in the intensity and frequency of extreme snowfall events show a~distinct difference spatially. In general, trends of five indices were found shifting from decreasing to increasing from the northeast to the southwest and from the north to the south of northern Xinjiang. Furthermore, the regions covered by increasing or decreasing extreme snowfall events were identified, implying the hot or cold spots for extreme snowfall events changes. These results may be helpful for northern Xinjiang on the regional and local resource and emergency planning.

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