Author(s): Juhee Kim; Subin Jeong; Yeonji Suh; Kyung-Lak Lee And Hyun-Han Kwon
Linked Author(s): Hyun-Han Kwon
Keywords: Benthic Macroinvertebrate Index Climate change scenario Leave-one-out cross-validation Multiple linear regression model Scenario-neutral
Abstract: Streamflow plays a critical role in providing habitats for aquatic organisms and significantly impacts water supply, flood control, and water quality improvement, all of which are closely linked to human well-being. However, climate change, through rising water temperatures, changes in precipitation patterns, and increasing sea levels, poses significant direct and indirect threats to aquatic ecosystems. These changes lead to biodiversity loss, habitat alterations, and shifts in ecological communities, highlighting the urgent need for monitoring and assessing ecosystem health in a changing climate. In South Korea, the National Institute of Environmental Research (NIER) uses five Streamflow Health Indices (SHIs) to assess the health of aquatic ecosystems: the Trophic Diatom Index (TDI), Benthic Macroinvertebrate Index (BMI), Fish Assessment Index (FAI), Habitat and Riparian Index (HRI), and Riparian Vegetation Index (RVI). This study focuses on the BMI, exploring its relationship with hydrological, meteorological, and environmental factors. We incorporated lag analysis to determine the optimal delay for predictive variables. A multiple linear regression (MLR) model to predict BMI is developed within a Leave-One-Out Cross-Validation (LOOCV) framework. Furthermore, this study explored the direct and indirect effects of climate change on the BMI by projecting average BMI value over time using a 5-year moving window, informed by the prediction model and various climate change scenarios.
Year: 2025