The Yellow River basin becomes wetter or drier? The case as indicated by mean precipitation and extremes during 1961–2012
The Yellow River basin becomes wetter or drier? The case as indicated by mean precipitation and extremes during 1961–2012
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DOI：
10.1007/s00704-014-1138-7
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发表时间：
2015-02
影响因子：
3.4
通讯作者：
K. Liang;Sheng‐Ao Liu;P. Bai;Rong Nie
中科院分区：
文献类型：
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作者：
K. Liang;Sheng‐Ao Liu;P. Bai;Rong Nie
黄河流域可划分为三个亚区，是研究区域气候变化的理想对象。利用黄河流域62个气象站的逐日降水资料，分析了1961-2012年黄河流域年、月平均降水量和极端降水量的空间分布和时间变化趋势。Mann-Kendall趋势检验和线性最小二乘法用于检测年和月平均降水量和极端值的趋势和幅度。结果表明，平均降水量和极端降水量的变化趋势各不相同，且三个亚区的变化趋势也各不相同。年平均降水量在整个流域略有减少，趋势为-8.8毫米/十年，在东部季风分区显著减少，趋势为-14.4毫米/十年，但在高海拔分区增加，趋势为1.3毫米/十年。黄河流域各月降水量呈现不同的季节变化规律，12月和6月的正趋势最大，11月和10月的负趋势最大。黄河流域年极端降水量的变化程度总体呈下降趋势，表现为CWD、SDII、R10、R95 p、R99 p、Rx 1day和Rx5 day等7个指标均呈负向变化，但3个亚区差异显著。其中，东部季风区10个极端降水指数的变化趋势均为负向，而干旱半干旱区和高海拔区则以正向为主。4个月降水指数（PRCPTOT、SDII、Rx 1day和Rx5 day）在2月、5月、6月和12月以正趋势为主，4月、8月、9月、10月和11月以负趋势为主，其中正趋势最显著的月份（2月或6月或12月）明显不同，负趋势最显著的月份（11月）相同。在全流域范围内，PRCPTOT、SDII、R10、R20、R95 p、R99 p、Rx 1day和Rx5 day等8个指标与海拔高度呈正相关，CDD和CWD等2个指标与海拔高度呈负相关，但在3个亚区，10个指标与海拔高度的相关性均不显著。极端指数与大尺度大气环流的关系表明，在整个流域内，10个极端指数与北方半球副热带高压（NHSH）和北方半球极涡（NHPV）的关系不大。但对于四个月降水指数（即，Rx 1day、Rx5 day、PRCPTOT和SDII）与NHSH呈显著正相关，与NHPV呈显著负相关。研究结果有助于掌握当地平均降水量和极端降水量的变化规律，对预防极端降水造成的自然灾害具有重要意义。
The Yellow River basin could be divided into three sub-regions, which makes it as the ideal target for studying regional climate change. On the basis of daily precipitation at 62 meteorological stations in the Yellow River basin, spatial distribution and temporal trends of annual and monthly mean precipitation and extremes were analyzed during 1961–2012. The Mann–Kendall trend test and linear least-square method were used to detect trends and magnitudes in annual and monthly mean precipitation and extremes. The results indicate that mean precipitation and extremes have different trends, and the three sub-regions also have distinct trends. Annual average precipitation shows a slight decrease in the whole basin with a trend of −8.8 mm/decade, a significant decrease in the eastern monsoon sub-region with trends of −14.4 mm/decade but increases in the high-elevation sub-region with trends of 1.3 mm/decade. Monthly precipitation in the Yellow River basin shows a different seasonality, December and June have largest positive trends, while November and October have largest negative trends. The change degree of annual precipitation extremes in the whole Yellow River basin decreased, reflected by seven indices (CWD, SDII, R10, R95p, R99p, Rx1day, and Rx5day) having negative trends but significantly different in the three sub-regions. Specifically, trends of all the ten annual precipitation extremes indices in the eastern monsoon sub-region were dominant negative, while mainly positive in the arid and semi-arid sub-region and high-elevation sub-region. The four monthly precipitation indices (PRCPTOT, SDII, Rx1day, and Rx5day) have main positive trends in February, May, June, and December, while negative trends in April, August, September, October, and November, in which the months having the most dominant positive trends are distinctly different (in February or June or December), while months with the most dominant negative trends are the same (in November). In the whole basin, eight indices (PRCPTOT, SDII, R10, R20, R95p, R99p, Rx1day, and Rx5day) have positive relations with elevation, while two indices (CDD and CWD) have negative relationship with elevation, but in the three sub-regions, relations between the ten indices and elevation are not significant. Relationship between extremes indices and large-scale atmospheric circulations show that, in the whole basin, all the ten annual indices have little relationship with Northern Hemisphere Subtropical High (NHSH) and Northern Hemisphere Polar Vortex (NHPV). But for the four monthly precipitation indices (i.e., Rx1day, Rx5day, PRCPTOT, and SDII), there were significant positive relationships with NHSH but significant negative relationships with NHPV. The results of this study are useful to master change rule of local mean precipitation and extremes change, which will help to prevent natural hazards caused by precipitation extremes.