Spatio-temporal evolutions of precipitation in the Yellow River basin of China from 1981 to 2013
Spatio-temporal evolutions of precipitation in the Yellow River basin of China from 1981 to 2013
复制标题
1981—2013年中国黄河流域降水时空演变
DOI：
10.2166/ws.2016.072
复制
发表时间：
2016-10
期刊：
影响因子：
--
通讯作者：
Ma, Xiaoyi
中科院分区：
文献类型：
--
作者：
Wu, Lei;Liu, Xia;Ma, Xiaoyi
利用黄河流域数字高程模型，采用部分薄板平滑样条法评价了1981 - 2013年黄河流域降水的时空变化趋势。结果表明：(1)样条法可大大提高气象数据的空间插值精度，可应用于地形复杂的大区域气象要素空间建模；(2)总体上，黄河流域年降水量呈现自东向西递减的趋势，400 mm等雨量当量分布与中国半湿润半干旱区分界线基本一致。四川的年平均降水量最多，内蒙古的年平均降水量最少；山东、河南年降水量变化较为显著，甘肃、青海年降水量变化较小；(3) 1981—2013年流域月平均降水量总体上呈现自北向南增加的趋势；各省年降水主要集中在全年的7-9个月。大部分省份的月平均降水量最高出现在7月，宁夏最高出现在8月。研究结果可为认识未来降水变化和提高人类应对气候变化的能力提供科学依据。
The partial thin plate smoothing splines method was applied to evaluate spatiotemporal trends of precipitation in the Yellow River basin from 1981 to 2013 by considering the basin9s digital elevation model. Results indicate that: (1) the spline method can greatly improve the spatial interpolation accuracy of meteorological data, and can be applied to spatial modeling of meteorological elements for large regions with complex terrain; (2) Overall, annual precipitation in the Yellow River basin shows a decreasing trend from east to west, with the isohyet equipluve distribution of 400 mm being basically consistent with the dividing line between semi-humid and semi-arid regions in China. Sichuan has the highest annual average precipitation while Neimeng has the lowest; Shandong and Henan both have relatively significant changes of annual precipitation while Gansu and Qinghai have small changes; (3) The average monthly precipitation of the basin from 1981 to 2013 presents an overall increasing trend from north to south; The annual precipitation in each province was mainly concentrated in 7–9 months of the whole year. The highest monthly average precipitation in most provinces occurred in July, while the highest at Ningxia occurred in August. Results may provide a scientific basis for understanding future precipitation changes and improving our ability to cope with climate change.
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