Hydrologic alteration along the Middle and Upper East River (Dongjiang) basin, South China: a visually enhanced mining on the results of RVA method

Hydrologic alteration along the Middle and Upper East River (Dongjiang) basin, South China: a visually enhanced mining on the results of RVA method
复制标题

DOI:
10.1007/s00477-008-0294-7
复制
发表时间:
2010
影响因子:
4.2
通讯作者:
Y. Chen;Tao Yang;Chong-yu Xu;Qiang Zhang;Xi Chen;Z. Hao
Y. Chen;Tao Yang;Chong-yu Xu;Qiang Zhang;Xi Chen;Z. Hao
中科院分区:
环境科学与生态学3区
文献类型:
--
作者:
Y. Chen;Tao Yang;Chong-yu Xu;Qiang Zhang;Xi Chen;Z. Hao

文献摘要

被引文献

相似文献

本文利用变异范围法(RVA)和可视化软件包 XmdvTool,对 1952 年至 2002 年华南东河中上游大坝引起的水文变化的时空模式进行了视觉增强评估。气候变化对水文过程的影响已分别在湿润期和干旱期被消除,因此我们重点关注人类活动(即大坝建设)的影响。研究结果表明:(1)大坝极大地改变了东河沿线的自然流态、范围条件和空间变化; (2) 1952-2002年东河平均流量上升率(1.16)、3日最大值(0.91)、低脉冲持续时间(0.88)、1月(0.80)、7月(0.80)和2月(0.79)是大坝建设引起的水文变化最显着的6个指标; (3)复活节河沿线3个站点水文时空变化存在差异。受上游建坝影响,岭夏、河源至龙川站水文变化程度加大。这项研究表明,高维水文数据集的可视化技术与 RVA 结合有利于检测时空水文变化。
This paper presents a visually enhanced evaluation of the spatio-temporal patterns of the dam-induced hydrologic alteration in the middle and upper East River, south China over 1952–2002, using the range of variability approach (RVA) and visualization package XmdvTool. The impacts of climate variability on hydrological processes have been removed for wet and dry periods, respectively, so that we focus on the impacts of human activities (i.e., dam construction). The results indicate that: (1) along the East River, dams have greatly altered the natural flow regime, range condition and spatial variability; (2) six most remarkable indicators of hydrologic alteration induced by dam-construction are rise rate (1.16), 3-day maximum (0.91), low pulse duration (0.88), January (0.80), July (0.80) and February (0.79) mean flow of the East River during 1952–2002; and (3) spatiotemporal hydrologic alterations are different among three stations along Easter River. Under the influence of dam construction in the upstream, the degree of hydrologic changes from Lingxia, Heyuan to Longchuan station increases. This study reveals that visualization techniques for high-dimensional hydrological datasets together with RVA are beneficial for detecting spatio-temporal hydrologic changes.