## 5 Conclusions  
Investigating the spatial and temporal dynamic behavior of the groundwater level fluctuation in mountainous areas can assist in obtaining effective strategies for developing and managing groundwater resources. This study used long-term groundwater level monitoring data in the mountainous area of Taiwan to analyze GWLF potentials during the wet and dry seasons and developed a technique for mapping the spatial distribution of GWLF potential. The main findings are summarized as follows:  
1. This study analyzed the groundwater level data from 18 monitoring stations for eight hydrological years. In the dry season, the groundwater level changed from 0.45 m to 6.35 m. In the wet season, the groundwater level changed from 1.30 m to 18.67 m. In addition, the spatial variations in the groundwater levels may not be consistent from year to year. This exception is presumed to be related to the lateral recharge behavior in the mountain area. However, this point should be carefully inspected by comparing the lateral flow data. 2. The eight proposed environmental influencing factors, including slope, drainage density, land use, lithology, hydraulic conductivity, porosity, groundwater depth, and regolith thickness, affect GWLF potential. The contribution of each factor to GWLF potential is adjusted according to the amount of rainfall (or groundwater recharge), which is a rather complex mechanism. Although the types of data collected so far do not fully reveal this mechanism, the main controlling factors affecting groundwater level fluctuations in wet and dry seasons can be roughly obtained. In the wet season, the factors that significantly influence GWLF include K, GW, LT, D, and RT. In the dry season, the factors that significantly influence GWLF include LT, S, and P. Thus, the dominant controlling factors are not precisely the same between the wet and dry seasons. 3. To verify the accuracy of the estimated GWLF results, the simulated results were compared with the observed GWLF from 18 groundwater monitoring wells with eight years of data. Overall, the verification results from 144 measurements demonstrate that the developed model can be expected to predict the GWLF for different seasons with a \"good\" level of accuracy, based on [PERSON]'s [32] guidelines of error for calibration/verification tolerances to watershed model users. Therefore, the developed model can predict the spatial GWLF distribution based on the groundwater level data from a few wells. However, it should be noted that the proposed model needs to collect more data and other types of data (e.g., lateral discharge measurements) to improve its prediction ability. 4. By comparing the spatial distribution of rainfall with the GWLF data, the groundwater level changes with the seasonal rainfall for most of the wells, which can lead to the inference that the groundwater circulation is pronounced. However, the groundwater level will soon return to its normal groundwater level while rainfall stops. This implies that the aquifer cannot store groundwater easily in the mountainous area of Taiwan. In addition, the time required for the groundwater level to return to the  
Figure 17: Variation in daily rainfall and groundwater elevation from November 2017 to October 2019 in borehole BHW-09. pre-rainfall level may take a few minutes to several days. The difference may be due to the rainwater infiltrating into the saturated aquifer, which can be affected by the geomorphological, geological, and hydrogeological characteristics. * The core of the proposed method is to obtain the weighting coefficients of the influencing factors affecting the GWLF potential by using the in situ hydrogeological test data and groundwater level data. Compared to the previous methods of using only static hydrogeological data and constant weights, the newly developed method further strengthens the input of the dynamic behavior of groundwater into the estimation of groundwater level fluctuation potential.