摘要
为预测不同径流过程影响下的盐水入侵强度,以长江口南支上段为研究对象,采用实测资料和理论分析相结合,建立了以大通流量和农历日期快速估算氯度值的经验模型。首先,以徐六泾实测潮位资料结合调和分析理论,在考虑11个主要分潮情况下证明日均潮差为半月周期函数,提出了用农历日期估算日均潮差的方法;其次,采用东风西沙实测氯度资料,选用不同函数形式,分析了以支汊盐水倒灌为主的情况下日均氯度对径流、潮差的量化响应关系;最后,提出了指数函数形式的氯度预测经验模型,模型计算值与实测值之间的决定系数在0.8以上。提出的经验模型可由大通流量快速估算特定位置的盐度,为相关的工程和规划研究提供了便捷途径。
To predict the intensity of saltwater intrusion under different riverine discharge, a new empirical model was developed to predict the chlorinity in the upper South Branch of the Yangtze River Estuary, where saltwater intrusion is strongly affected by tidal flow from the North Branch. In the model, only runoff at Datong station and lunar calendar date were required as variables. Firstly, the changes of daily mean tidal range was investigated with measuring data at Xuliujing. Using the theory of harmonic analysis with 11 main tidal components, it was proved that the daily mean tidal range changes every half-month. Thus, a new method was proposed to estimate the daily mean tidal range by using the lunar calendar. Secondly, the quantitative relationships between chlorinity and riverine discharge, tidal range were developed using the measured data at Dongfengxisha. Some functions in previous empirical models were examined to test their effectiveness under the condition of saltwater intrusion from the sub-branch channel. Finally, an empirical model was proposed in the form of exponential function to describe the chlorinity under different riverine discharge and tidal range conditions. The predicted chlorinity process at Dongfengxisha was in good agreement with the observations, with the correlation degree of up to 0.8. Using the proposed model in this study, intensity of saltwater intrusion at a certain location can be estimated with simple input data, which provides a convenient method to conduct prediction of saltwater intrusion.
出处
《水科学进展》
EI
CAS
CSCD
北大核心
2017年第2期213-222,共10页
Advances in Water Science
基金
国家水体污染控制与治理科技重大专项(2014ZX07104-005)~~
关键词
长江口
盐水倒灌
径流
潮差
预测模型
Yangtze River Estuary
saltwater intrusion
riverine discharge
tidal range
predictive model