摘要
根据秦岭-黄淮平原交界带及其邻近地区42个径流站1962~1999年的观测资料,分析了地表径流量及其时间变化(年内分配和年际变化)的空间分布规律,得出二者之间存在正相关关系的结论,并探讨了这种反常现象的成因。
There is generally an inverse interrelated relationship between surface runoff and its time variations (seasonal and annual variation) in most areas of the world and China, but it is direct interrelated relationship in the joint zone between Qinling mountains and Huanghuai Plain. Based on the 1962-1999 observation data of 42 runoff stations in the joint zone and its adjoining area, we get the following results: the distribution lows of the surface runoff depth is that its value increases progressively from north to south and from plain to mountain, the distribution lows the proportion of winter runoff and annual runoff R_w/R is that its value increases progressively from south to north and from mountain to plain, the distribution lows the proportion of the summer runoff and annual runoff R_s/R and flood season runoff and annual runoff R_f/R are that their values increase progressively from north to south and from plain to mountain, and these results shows that there is a direct interrelated relationship between the surface runoff and its seasonal variation; both the proportion of extreme value of annual runoff K_m and the variation coefficient of annual runoff C_v have the distribution lows that their values increase progressively from north to south and from plain to mountain, and these results shows that there is a direct interrelated relationship between the surface runoff and its annual variation too. The main causes of formation of the abnormal phenomenon are that the surface runoff depths of different areas have less differences in winters and wet years, and have much differences in summers and dry years. In other words, the differences of annual runoff of different areas formed mainly in summers and wet years.
出处
《地理科学》
CSCD
北大核心
2004年第3期281-285,共5页
Scientia Geographica Sinica
关键词
秦岭-黄准平原交界带
地表径流量
时间变化
正相关关系
joint zone between Qinling Mountains and Huanghuai Plain
surface Runoff
time variations
direct interrelated relationships