Long-Term Evolution of Cones of Depression in Shallow Aquifers in the North China Plain

Water

Yuan Zhang¹ 

[1] Key Laboratory of Engineering Geomechanics, Institute of Geology and Geophysics, Chinese Academy of Sciences, No.19 Beitucheng West Road, Chaoyang District, P.O. Box 9825, Beijing 100029, China; E-Mail:

关键词: numerical simulation;    South-to-North water diversion;    neural network algorithm;    simulated annealing algorithm;   

DOI  :  10.3390/w5020677

来源: mdpi

【 摘 要 】

The North China Plain (NCP) is one of the places where the groundwater is most over-exploited in the world. Currently, our understanding on the spatiotemporal variability of the cones of depression in this region is fragmentary. This study intends to simulate the cones of depression in the shallow aquifer across the entire NCP during the whole period from 1960 to 2011. During the simulation, the dominant role of anthropogenic activities is emphasized and carefully taken into account using a Neural Network Algorithm. The results show that cones of depression in the NCP were formed in 1970s and continuously expanded. Their centers were getting deeper with an increasing degree of groundwater exploitation. This simulation provides valuable insights for developing more sustainable groundwater management options after the implementation of the South-to-North Water Diversion Project (SNWDP), which is a very important surface water project in China in the near future. The numerical model in this paper is built by MODFLOW, with pumpage data completed by neural network algorithm and hydrogeological parameters calibrated by simulated annealing algorithm. Based on our long-term numerical model for regional groundwater flow in the NCP, one exploitation limitation strategy after the implementation of SNWDP is studied in this paper. The results indicate that the SNWDP is beneficial for groundwater recovery in the NCP. A number of immense groundwater cones will gradually shrink. However, the recovery of the groundwater environment in the NCP will require a long time.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.