인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Given the pressures on water resources caused by global climate change and human activities, the assessment and management of groundwater resources in mountainous region have become increasingly important. The central mountainous region of Taiwan, as one of the significant sources of groundwater recharge, plays a critical role in overall water resource management due to its groundwater storage capacity and recharge capability. Addressing the challenges of limited survey and observational data in mountainous groundwater assessments, this study uses the lumped parameter groundwater model AquiMod to analyze long-term groundwater level changes at 23 monitoring stations in mountainous areas of central Taiwan. This study is based on long-term groundwater level monitoring data (2010-2021) analyzing the relationship between groundwater levels and precipitation, and performs model calibration and prediction. The results indicate a strong correlation between groundwater levels in mountainous areas and precipitation. While the model predictions were satisfactory for most monitoring stations, obtaining Nash Sutcliffe efficiency scores of between 0.5 and 0.9 at 14 of the 23 monitoring stations. However, poorer performance at several stations reflects limitations arising from data gaps, complex local geology, and the inability of the lumped model to represent lateral recharge or anthropogenic influences. Model sensitivity analysis further highlights the critical role of unsaturated zone parameters, such as rooting depth, soil storage and upper-layer saturated hydraulic conductivity, in shaping groundwater responses. In summary, the lumped parameter groundwater model has proven practical for evaluating groundwater in Taiwan's mountainous regions and can serve as a reference for the sustainable management of future water resources.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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