Spatial and temporal variation characteristics of the drought index in China grasslands in the recent 40 years （1982—2018）
- Vol. 26, Issue 12, Pages: 2629-2641(2022)
DOI： 10.11834/jrs.20220433
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摘要
在全球变暖影响下，由降水、气温等气象要素所主导的干湿条件在全球或区域上均发生了明显变化，中国草原区对气候变化敏感、具有明显的生态脆弱性特征，因此研究中国草原区的干湿变化趋势非常重要。本研究基于干旱指数分析了1982年—2018年中国草原区的干湿程度时空变化趋势。结果表明，1982年—2018年中国草原区的干旱指数呈不显著上升趋势。2005年前后两个时期中国草原区表现出显著的趋势差异，1982年—2005年中国草原区干旱指数呈下降趋势，即干旱程度增加，而2006年—2018年呈增加趋势，该时段湿润化明显。2006年之后的中国草原区湿润化趋势源于该地区降水持续增多，而增温停滞导致潜在蒸散发不再升高。分区域来看，1982年—2005年内蒙古草原区干旱指数呈下降趋势，2006年—2018年呈上升趋势；西北草原区在1982年—2005年和2006年—2018年两个时间段干旱指数均呈上升趋势；青藏草原区在1982年—1994年干旱指数呈下降趋势，1995年—2018年呈上升趋势。
Abstract
The dry/wet conditions dominated by precipitation, air temperature, and other meteorological factors have globally or regionally changed as a result of global warming. Grasslands cover around 40% of China and are vulnerable to climate change and ecological susceptibility. Accordingly, the dry/wet condition change trend of grasslands in China must be studied. Many studies on drought have been conducted in China, but two defects continue to persist: (1) most studies did not take into account the dry and wet changes of grassland in China as a whole; (2) the time range of research has not been extended to recent years. This study analyzed the temporal variation of drought-wet degree and its causes in this region during 1982—2018 based on the drought index and meteorological factors. The optimal drought index is the maximum correlation coefficient of soil moisture and multi drought index, and the optimal drought index was used for subsequent analysis. The piecewise regression approach was used to examine whether a turning point of the trend of drought index developed, and ordinary least squares was used to test the significance. The least square method was used to estimate the trend of the drought index. Pearson correlation analyses were conducted to quantify the relationship between drought index and climatic factors. Our results indicated the drought index based on the station ratio of precipitation and GLEAM potential evapotranspiration, which can reflect the change of dry/wet degree in China’s grasslands. The drought index had no significant increase trend from 1982 to 2018, and a trend shift occurred in 2005. The drought index of grasslands in China decreased by -0.0005 a,-1,from 1982 to 2005 and increased by 0.009 a,-1, from 2006 to 2018. The reason is that the increased water consumption causes increased the temperature and enhanced the evapotranspiration from 1982 to 2005. The water consumption of evapotranspiration was alleviated from 2006 to 2018 due to the continuous increase in precipitation and stagnation of temperature increase. The drought index of Mongolia grasslands showed a decreasing trend from 1982 to 2005 and an increasing trend from 2006 to 2018. Meanwhile, the drought index of Northwest grasslands showed an increasing trend from 1982 to 2005 and 2006 to 2018, respectively. The drought index of the Tibetan plateau showed a decreasing trend from 1982 to1994, and an increasing trend from 1995 to 2018. The drought index trend change was positively correlated with precipitation in China grasslands. The drought index was a ratio of precipitation on station and GLEAM PET, which can reflect the temporal dynamics of the dry/wet conditions of grasslands in China. The grasslands in China showed a drought trend from 1982 to 2005 and a wet trend from 2006 to 2018. The turning point year of Mongolia and Northwest grasslands are the same. The Mongolia grasslands and Tibetan plateau transitioned from dry to wet between before and after turning point in two periods. Meanwhile, the Northwest grasslands experienced continuous wetness in two periods. The changes of dry/wet in China grasslands was mainly dominated by Mongolia grasslands. Moreover, the change of trend is mainly dominated by precipitation.
关键词
遥感中国草原区干旱指数动态变化转变气候变化
Keywords
remote sensingChina grasslandsdrought indexdynamic changeshiftclimate change
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School of Geography, South China Normal University, Shipai Campus
University of Chinese Academy of Sciences
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS
Department of Resource and Environmental Sciences, Beijing Normal University
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