School of Ocean and Earth Sciences, Jeju National University, 102 Jejudaehak-ro, Jeju-si, Jeju Special Self-Governing Province 63243, Republic of Korea.
Korea Institute of Geoscience and Mineral Resources, 124 Gwahak-ro, Yuseong-gu, Daejeon 34132, Republic of Korea.
Sci Total Environ. 2022 Dec 20;853:158619. doi: 10.1016/j.scitotenv.2022.158619. Epub 2022 Sep 6.
In this study, the combined use of a Bayesian mixing model (BMM), numerical model (random walk particle tracking-RWPT), and environmental tracers (δO-δD, H, and CFC) was applied to elucidate the probabilistic contribution of the recharge sources, flow path, and residence time of groundwater across the mountainous area of Jeju Island, South Korea. Especially, the BMM ability to estimate the variable recharge contributions to the aquifer by different elevations and seasons was investigated. The δO-δD isotopes showed that groundwater in the study area was primarily fed by precipitation during the wet season, and the BMM estimated that wet season recharge contributed to approximately 64% of the total. The BMM-based probabilistic estimation of recharge sources revealed a mixed contribution of source waters from different elevations. A notable difference in recharge flow path was observed between highland (>450 masl) and lowland (<400 masl) wells, where the inflow of source water from the regional flow was dominant in the former and both regional and local recharges served as significant groundwater sources in the latter. Evidence from age tracers (H and CFC-12) also supported different recharge mechanisms between highland and lowland wells. A reasonable match between the BMM- and RWPT-derived recharge contributions (RMSE 0.02-0.06) was achieved within the uncertainty ranges, with RWPT being particularly useful for capturing different flow paths between highland and lowland wells. The dynamics revealed here provide important information for establishing an improved and informed groundwater management plan for the mountainous area of Jeju Island. Ultimately, this study highlights the advantageous integrated analysis of BMM, RWPT, and environmental tracer analyses to enhance the reliability of recharge area estimation and increase the collective understanding of complex hydrogeological systems in mountainous areas.
在这项研究中,我们联合使用贝叶斯混合模型(BMM)、数值模型(随机游走粒子跟踪-RWPT)和环境示踪剂(δO-δD、H 和 CFC)来阐明韩国济州岛山区地下水补给来源、水流路径和停留时间的概率贡献。特别是,研究了 BMM 估计不同海拔和季节下可变补给对含水层的贡献的能力。δO-δD 同位素表明,研究区地下水主要由雨季降水补给,BMM 估计雨季补给约占总补给的 64%。基于 BMM 的补给源概率估计揭示了不同海拔水源的混合贡献。高海拔(>450 masl)和低海拔(<400 masl)井之间的补给水流路径存在显著差异,前者的源水主要来自区域流,后者的区域和局部补给都是重要的地下水来源。年龄示踪剂(H 和 CFC-12)的证据也支持高海拔和低海拔井之间的不同补给机制。BMM 和 RWPT 衍生的补给贡献之间存在合理的匹配(RMSE 0.02-0.06),在不确定性范围内,RWPT 特别有助于捕捉高海拔和低海拔井之间的不同水流路径。这里揭示的动态为建立济州岛山区改进和知情的地下水管理计划提供了重要信息。最终,本研究强调了 BMM、RWPT 和环境示踪剂分析的优势集成分析,以提高补给区估计的可靠性,并提高对山区复杂水文地质系统的综合理解。