Daily snow water equivalent product with SMMR, SSM/I and SSMIS from 1980 to 2020 over China

IF 4.2 3区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Big Earth Data Pub Date : 2022-02-17 DOI:10.1080/20964471.2022.2032998
Lingmei Jiang, Jianwei Yang, Cheng Zhang, Shengli Wu, Z. Li, L. Dai, Xiaofeng Li, Y. Qiu
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引用次数: 7

Abstract

ABSTRACT The reliable knowledge of seasonal snow volume and its trend is very important to understand Earth’s climate system. Thus, a long-time snow water equivalent (SWE) dataset is necessary. This work presents a daily SWE product of 1980–2020 with a linear unmixing method through passive microwave data including SMMR, SSM/I and SSMIS over China after cross-calibration and bias-correction. The unbiased root-mean-square error of snow depth is about 5–7 cm, corresponding to 10–15 mm for SWE, when compared with stations measurements and field snow course data. The spatial patterns and trends of SWE over China present significant regional differences. The overall slope trend presented an insignificant decreasing pattern during 1980–2020 over China; however, there is an obvious fluctuation, i.e. a significant decrease trend during the period 1980–1990, an upward trend from 2005 to 2009, a significant downward trend from 2009 to 2018. The increase of SWE occurred in the Northeast Plain, with an increase trend of 0.2 mm per year. Whereas in the Hengduan Mountains, it presented a downward trend of SWE, up to −0.3 mm per year. In the North Xinjiang, SWE has an increasing trend in the Junggar Basin, while it shows a decreasing trend in the Tianshan and Altai Mountains.
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1980 - 2020年中国SMMR、SSM/I和SSMIS的日雪水当量产品
摘要:季节积雪量及其变化趋势的可靠数据对了解地球气候系统具有重要意义。因此,需要一个长期的雪水当量(SWE)数据集。本文利用SMMR、SSM/I和SSMIS等无源微波数据,经过交叉定标和偏置校正,采用线性解混方法,给出了1980-2020年中国的日SWE产品。积雪深度的无偏均方根误差约为5 ~ 7 cm,相当于SWE与台站测量和现场雪道数据的10 ~ 15 mm。中国SWE的空间格局和趋势存在显著的区域差异。1980—2020年,中国的总体坡度趋势呈不显著的下降趋势;但存在明显的波动,即1980-1990年呈明显下降趋势,2005 - 2009年呈上升趋势,2009 - 2018年呈明显下降趋势。SWE增加主要发生在东北平原,增加趋势为0.2 mm / a。横断山区SWE呈下降趋势,最高可达- 0.3 mm /年。在北疆,准噶尔盆地SWE呈增加趋势,天山和阿尔泰山SWE呈减少趋势。
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来源期刊
Big Earth Data
Big Earth Data Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
7.40
自引率
10.00%
发文量
60
审稿时长
10 weeks
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