Investigating the Mechanisms of Groundwater Level Variation and Recharge at Zhuoshui River Basin

• Main Authors: Wan-Yu Chang, 張琬渝
• Other Authors: 張斐章
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/40412661410156005702

碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 101 === In Taiwan, most of rainfalls go straight into the ocean. Rainfall cannot be utilized efficiently due to topographical limitations and non-uniformly distributed rainfall patterns. Therefore, groundwater has become an important water source during drought periods and/or at the areas short of water storage facilities due to the low-cost and easy accessibility of groundwater. How to preserve and recharge groundwater effectively has become an important issue. The mountainous areas and the proximal-fan areas of the Jhuoshui River basin in Central Taiwan have been considered good groundwater recharge areas. However few researches on the recharge mechanisms in the mountainous areas of the Jhuoshui River basin can be found, therefore it is necessary to investigate the relationship between surface water and groundwater and to construct groundwater models at this area. This study investigates the interactive mechanisms between surface water and groundwater, and the mountainous areas as well as the proximal-fan areas of the Jhuoshui River basin in Central Taiwan is the study area. This study first investigates the mechanisms that result in the variations of groundwater levels and then focuses on the influence of surface water on groundwater level variations. Statistics methods are adopted to analyze the correlations between cumulative rainfall and groundwater level variation at groundwater monitoring wells, and the effective rainfall thresholds that cause efficient groundwater recharge activities can be identified. The results indicate that it requires accumulated rainfall of several days to make groundwater levels variable at low-permeability wells or deep wells. This study next adopts the Gamma Test (GT) to select the critical input factors to the ANN models. Then both the backpropagation neural network (BPNN) in consideration of its superior nonlinear mapping ability as well as high estimation accuracy and the adaptive network fuzzy inference system (ANFIS) with a fuzzy rule base are used to construct estimation models for groundwater level variations at groundwater monitoring wells. Finally, this study adopts the fuzzy inference system with spatial and geological information of groundwater monitoring wells to analyze the characteristics of groundwater recharge mechanisms and further classifies three kinds of groundwater monitoring wells with a similar mechanism of water level variation for each type. Results indicate that both BPNN and ANFIS estimation models perform well. This study also estimates the average groundwater recharge over the mountainous areas of Jhuoshui River basin, with an estimated annual amount of 1.04 billion of tons. In sum, this study investigates the rainfall and streamflow information in the Jhuoshui River basin, and further links the analytical results to groundwater level variations at groundwater monitoring wells. The results of this study can provide valuable information for the prevention as well as treatment of land subsidence and can be a good reference for water resources management in the Jhuoshui River basin.