| Title |
Mean Action Time as an Indicator of Aquifer Performance |
| Authors |
장선우(Chang, Sun Woo);이동섭(Rhee, Dong Sop);정일문(Chung, Il-Moon) |
| DOI |
https://doi.org/10.12652/Ksce.2025.45.5.0567 |
| Keywords |
평균작용시간 (MAT);지하수 반응시간;해수침투;대수층 동역학;지하수 관리 Mean Action Time (MAT);Groundwater response time;Seawater intrusion;Aquifer dynamics;Groundwater management |
| Abstract |
Understanding aquifer responses to hydrological perturbations is essential for sustainable groundwater management. The Mean Action Time (MAT) is a statistical metric derived from the first temporal moment of a system’s response function, representing the characteristic time during which the effects of a disturbance are prominently expressed. MAT effectively captures aquifer response dynamics, even under conditions of gradual transitions or incomplete convergence. When derived directly from experimental or observational data, MAT inherently reflects the heterogeneity of the aquifer medium while simultaneously accounting for nonlinear processes and uncertain boundary conditions. This allows a more comprehensive assessment of the actual dynamic behavior of groundwater systems, which may not be adequately captured by simplified numerical models or analytical approximations. In this study, data from laboratory-scale experiments by Chang and Clement (2012) that reproduced the dynamic response of a coastal aquifer with a freshwater-seawater interface were used. MAT was calculated based on the observed movement of the the interface during both advancing and receding phases. In addition, auxiliary indicators associated with MAT, such as the Variance of Action Time (VAT) and the 99 % response time, were computed to compare aquifer response times and patterns, enabling quantitative evaluation of aquifer performance and stability. The results demonstrate that MAT provides a useful indicator not only for seawater intrusion and freshwater recovery processes but also for contaminant transport assessment, infrastructure management, and broader groundwater management decision-making. This approach integrates experimental and field observations to evaluate aquifer dynamics and offers a novel analytical framework that enhances predictive reliability. |