Kyoung Jae Lim

This study investigated the effects of nonpoint source (NPS) pollution reduction and pollutant dynamics in a highland agricultural watershed in Korea. We employed the SWAT model to simulate hydrological processes and pollution transport within the watershed. The model incorporates future climatic scenarios derived from downscaled climate projections to assess their impacts on NPS pollution and pollutant reduction methods. These changes lead to heightened surface runoff and erosion rates, resulting in elevated sediment and nutrient concentrations. The projection indicates an anticipated increase in the annual average temperature by 1.3 to 2.1 °C by the mid-century, under scenarios SSP126 and SSP585. Additionally, precipitation levels are projected to increase by 31 to 61 mm from the baseline to the end of the century. Variations in hydrological components such as evapotranspiration, streamflow, and soil moisture are expected to range from +3.2 to +17.2%, −9.1 to +8.1%, and 0.1 to 0.7%, respectively, during the years 2040 and 2080. Fluctuations in TN, SS, and TP loading are estimated to range from −4.5 to +2.3%, −5.8 to +29.0%, and +3.7 to +17.4%, respectively. This study emphasizes the importance of adaptive management options for stakeholders and the need for adaptive management options to reduce nonpoint source pollution and protect water quality to maintain sustainable water supplies and conserve the environment in this watershed.

This study addresses the challenge of accurately estimating hourly flow and soil moisture by integrating the Soil and Water Assessment Tool (SWAT) with a Land Surface Model (LSM). Our approach enhances SWAT by incorporating spatially distributed soil properties and a physically-based soil moisture process, using the Noah LSM for hourly soil moisture estimation. This integration captures spatial variations in soil moisture and hydraulic properties from remote sensing across the watershed. The parameter sensitivity analysis and the calibration of hourly flow were significantly impacted by the physically-based hourly soil moisture routing and the incorporation of spatially distributed soil properties. Consequently, the modified SWAT model showed improved accuracy in hourly flow simulations for long-term and multiple rainfall events. Validation results showed significant improvements, with Coefficient of Determination (R2) and Nash and Sutcliffe Efficiency (NSE) increasing by 25.95% and 33.3%, respectively, and Percent Bias (PBIAS) decreasing by 85.8%. Notably, the average error for peak flows across eight events decreased by 49%. These findings highlight the importance of initializing soil parameters based on spatial soil moisture distribution and incorporating physical process-based moisture routing to enhance hourly flow simulation accuracy. Future research should focus on validating the physical feasibility of the soil parameter set in the study area with detailed hourly flow and soil moisture data and exploring its applicability in various regions. This study provides valuable insights for the scientific community, water resources, and agricultural decision-makers regarding integrated modeling of soil moisture and hourly flow, which can inform dam operation management, disaster planning, and crop yield improvement.