5:15 PM - 7:15 PM
[AHW25-P09] Using multiple regression methods to establish an empirical formula for estimating groundwater recharge in Taiwan
Keywords:groundwater recharge , water-table fluctuation, multiple regression analysis
In recent years, climate change and global warming have led to an increasing frequency of extreme weather events, resulting in uneven rainfall distribution. The distinction between dry and wet seasons has become more pronounced, while the Earth's capacity for water storage remains limited. Compared to surface water, groundwater is less affected by seasonal precipitation variability and provides a more stable water supply. In 2022, groundwater consumption in Taiwan accounted for approximately 29.58% of the total water usage, underscoring the critical role of groundwater resources. However, groundwater recharge estimation has historically been subject to significant uncertainties due to the influence of complex hydrological factors.
This study integrates climate, soil, and hydrological data to develop a multiple regression analysis model using Python, aiming to establish an empirical framework for estimating groundwater recharge in Taiwan. The model's validity is assessed through the water-table fluctuation method, which is also employed to estimate recharge volumes across various groundwater regions in Taiwan. The verification process adheres to the water-table fluctuation formula. The study focuses on nine major groundwater regions in Taiwan, including the Taipei Basin, Taoyuan-Zhongli Tableland, Hsinchu-Miaoli Area, Taichung Area, Zhuoshui River Alluvial Fan, Chianan Plain, Pingtung Plain, Lanyang Plain, and the Hualien-Taitung Valley.
By enhancing the accuracy of groundwater recharge estimations, this study provides a robust and efficient methodological approach for rapid assessment across different regions. The findings contribute to improving groundwater resource management and mitigating the impacts of extreme climatic events on Taiwan’s water security.
This study integrates climate, soil, and hydrological data to develop a multiple regression analysis model using Python, aiming to establish an empirical framework for estimating groundwater recharge in Taiwan. The model's validity is assessed through the water-table fluctuation method, which is also employed to estimate recharge volumes across various groundwater regions in Taiwan. The verification process adheres to the water-table fluctuation formula. The study focuses on nine major groundwater regions in Taiwan, including the Taipei Basin, Taoyuan-Zhongli Tableland, Hsinchu-Miaoli Area, Taichung Area, Zhuoshui River Alluvial Fan, Chianan Plain, Pingtung Plain, Lanyang Plain, and the Hualien-Taitung Valley.
By enhancing the accuracy of groundwater recharge estimations, this study provides a robust and efficient methodological approach for rapid assessment across different regions. The findings contribute to improving groundwater resource management and mitigating the impacts of extreme climatic events on Taiwan’s water security.