Moonyoung Lee

Understanding regional as well as temporal variations in probability rainfall is essential for addressing climate change-related hydrological issues. Few studies have conducted spatial analyses on probability rainfall using up-to-date rainfall data, which is crucial to comprehend regional rainfall variations for effective flood management and hydraulic structure design. In this study, we analyzed the spatiotemporal variations of probability rainfall factors in South Korea using 61 rainfall stations and four rainfall periods (years) (recent-10, 2011–2020; recent-20, 2001–2020; recent-30, 1991–2020; recent-40, 1981–2020). We mapped probability rainfall information, including probability rainfall intensities (20, 30, and 40 mm/h), return periods (10, 20, 50, and 100 years), rainfall durations (1, 2, 6, and 24 h), and rainfall depth. Results revealed wide variations in the northern and southwest inland regions based on rainfall periods. Decadal annual rainfall analysis revealed that the north and southwest inland regions indicated lower recent decadal rainfall than that in previous decades, while decadal annual rainfall in the southeast inland region remained constant. The generated spatial and temporal distribution maps offer valuable insights for comprehending the variation in probability rainfall factors across different time periods in South Korea, with practical implications for the planning and design of hydraulic structures.

In aquatic ecosystems, flow is one of the most essential elements of aquatic species. It is necessary to explore the correlation with ecological indices for the management guidelines of aquatic ecosystems using flow because aquatic ecosystem data are limited. This study calculated the flow metrics using the flow and analyzed the correlation between the flow metrics and the ecological index. This study attempted to understand the correlation between the ecologic index and flow metrics. Flow metrics were quantified flow in various ways, depending on the size, frequency, and design of the flow. The characteristics of flow metrics were identified and the correlation with the ecological index was studied. The Pearson correlation coefficient values for 22 watersheds were compared using the flow data from 2008 to 2015 and the ecological index data from the BMI. In watersheds with high imperviousness, the Pearson correlation coefficient was negative, which indicated that the correlation in this study provides basic data for the quantitative evaluation of the river ecosystem by identifying the relationship between imperviousness and BMI. As a result, the highest Pearson correlation coefficient values of flow metrics were related to the flow coefficient of variation (MACV13-16; MHCV; MLCV).

The quality of water has deteriorated due to urbanization and the occurrence of urban stormwater runoff. To solve this problem, this study investigated the pollutant reduction effects from the geometric and hydrological factors of green infrastructures (GIs) to more accurately design GI models, and evaluated the factors that are required for such a design. Among several GIs, detention basins and retention ponds were evaluated. This study chose the inflow, outflow, total suspended solids (TSS), total phosphorus (TP), watershed area, GI area (bottom area in detention basins and permanent pool surface area in retention ponds), and GI volume (in both detention basins and retention ponds) for analysis and applied both ordinary least squares (OLS) regression and multiple linear regression (MLR). The geometric factors do not vary within each GI, but there may be a bias due to the number of stormwater events. To solve this problem, three methods that involved randomly extracting data with a certain range and excluding outliers were applied to the models. The accuracies of these OLS and MLR models were analyzed through the percentage bias (PBIAS), Nash-Sutcliffe efficiency (NSE), and RMSE-observations standard deviation ratio (RSR). The results of this study suggest that models which consider the influent concentration combined with the hydrological and GI geometric parameters have better correlations than models that consider only a single parameter.