The Jinan spring basin is located in the karst area of northern China, where springs serve as important sources of water supply. Several studies on spring protection and water supply have been carried out, and scholars have developed some laws on local groundwater flow dynamic and characteristics of aquifer structures. Unfortunately, there is a lack of detailed research on preferential recharge zones, which are the main recharge pathways of springs. Therefore, this research focuses on identifying preferential recharge zones based on the correlation between the spring level and precipitation. The results show that when precipitation is more intense or lasts longer, there is a stronger correlation between spring level and precipitation. It has been established that the precipitation at Donghongmiao station has the closest relationship with the dynamic of Baotu spring, which is found to be the most significant contribution to spring preservation. Two potential preferential recharge zones in the Jinan spring basin are detected through correlation analysis and geological exploration data. These findings support spring protection and water supply projects in karst regions.

Identifying the hydraulic properties of fracture networks is important for groundwater management of karst aquifers which are composed of fractured rocks and cavernous conduits. Although much research has been done on the geometrical characteristics and flow calculation of discrete fracture networks, further exploration on the relativity between them is still needed. This study aims to quantitatively analyze the relationship between different geometrical features and permeabilities of fracture networks based on the spring hydrographs, which can intuitively reflect the flow velocity of karstic medium system. We simulated rainfall-discharge processes of a karst area with different distributions of random fracture networks using a numerical model developed based on a laboratory experiment. The results of plenty of simulation show that the total length of fracture networks and relative density of fractures are most related to the peak values of spring discharge with the Pearson correlation coefficients of over 0.8, indicating that these two geometrical parameters can reflect the permeability of random fracture networks best, followed by the fractal dimension and number of intersection points. However, the connectivity of fracture networks also depends on whether there is one or more fractures that go through the entire study area, which greatly promote water movement and solute transport in the fractured rocks. Another factor that impacts the permeability of fracture networks is uniformity of distribution of fractures, in that a drastic propagation that occurs in a small area with clusters of fractures could not represent the overall permeability of fracture networks. Additionally, the surrounding rock matrix with ultralow hydraulic conductivity has a positive and significant impact on the water transmission capacity of fracture networks, showing that the strong water blocking effect of matrix pushes the groundwater movement towards fractures with high delivery capacity.   This study utilized the spring hydrographs to evaluate the permeability of fracture networks for convenience compared to the calculation of equivalent permeability coefficient, while the latter is more accurate and representative. The above findings can enhance the understanding of properties of fracture networks, benefit targeted observations of detailed structures of fractured rocks and then improve the efficiency of groundwater management in karst areas.