Sun-Kwon Yoon

This study assesses the characteristics of perennial streams using the dimensionless relationship between streamflow exceedance probability and the wet channel ratio based on a wet channel network extracted from light detection and ranging (LiDAR) data. LiDAR provides topographic data and signals’ intensity in high-resolution and with high accuracy to provide useful information for drainage networks and channel network extraction. In this study, a valley network and wet channel are extracted from LiDAR topographic and signals’ intensity information with a spatial resolution of 1 meter. Based on the available LiDAR data and streamflow observations from across the United States, we selected 30 watersheds with various climate conditions and analyzed the characteristics of their perennial streams. The wet channel ratio and perennial stream ratio were developed to define a perennial stream using the observed streamflow and the identified wet channel. The results of this study are consistent with previous studies on the definition of a perennial stream through transformation into a dimensionless form and confirmed the possibility of applying the wet channel ratio as an alternative parameter to define a perennial stream.

The purpose of this study is to identify the relationship between drainage density and climate, as represented by the climate aridity index, and to understand the relationship between drainage density and drainage area. A total of 121 study sites with low human impact, and a wide range of climate aridity index 0.3 (humid)–10.4 (arid), were selected based on the availability of light detection and ranging (LiDAR) data, producing a digital elevation model (DEM) with a spatial resolution of 1 m. A curvature-based method, incorporating both positive and negative curvature information, was used to extract the valley (drainage) network from the LiDAR-based DEMs. Drainage density and climate aridity index exhibited a monotonically increasing trend, contrary to the previous results that have shown a U-shaped relationship. This discrepancy was caused by the selection of watersheds with extensive human activity in the previous study. One-meter resolution DEM produced greater drainage density than the previous studies with a coarse spatial resolution of 30 m as small valleys are not detectable in low-resolution topography datasets. The discrepancy between the previous study and the current study results encouraged further investigation of the impact of the drainage area (watershed size). A negative correlation between drainage density and drainage area was reconfirmed, while a stronger decreasing trend was observed in arid regions than in humid regions.

It is necessary to predict dam inflow in advance for flood prevention and stable dam operations. Although predictive models using deep learning are increasingly studied, these existing studies have merely applied the models or adapted the model structure. In this study, data preprocessing and machine learning algorithms were improved to increase the accuracy of the predictive model. Data preprocessing was divided into two types: The learning method, which distinguishes between peak and off seasons, and the data normalization method. To search for a global solution, the model algorithm was improved by adding a random search algorithm to the gradient descent of the Multi-Layer Perceptron (MLP) method. This revised model was applied to the Soyang Dam Basin in South Korea, and deep learning-based discharge prediction was performed using historical data from 2004 to 2021. Data preprocessing improved the accuracy by up to 61.5%, and the revised model improved the accuracy by up to 40.3%. With the improved algorithm, the accuracy of dam inflow predictions increased to 89.4%. Based on these results, stable dam operation is possible through more accurate inflow predictions.

Non-linear behavioral links with atmospheric teleconnections were identified between the Indian Ocean Dipole (IOD) mode and seasonal precipitation over East Asia (EA) using statistical models. The analysis showed that the lower the lag time, the higher the correlation; more than a two-fold correlation for non-linear regression with a kernel density estimator than for the linear regression method. When the IOD peaked, a pattern of significant reductions in seasonal precipitation during the negative IOD period occurred throughout the Korean Peninsula (KP). The occurrence of the positive IOD was in line with the El Niño phenomenon and generated greater seasonal precipitation than only the positive IOD, which takes place from March to May. This change occurred more in the cold tongue El Niño than the warm pool El Niño, inducing much higher spring precipitation throughout the KP. When negative IODs and La Niña coincided, there was slightly greater precipitation from March to May compared to the sole occurrence of negative IODs. In positive (negative) IOD years, there was anti-cyclonic (cyclonic) circulation in the South China Sea (SCS), helping to transport moisture to EA. The composite precipitation anomalies in the positive (negative) IOD years show above (below) normal precipitation in southern China. In contrast, other parts of the EA experienced drier (humid) signals than normal years. In positive IOD years, the anti-cyclonic circulation strength of the Bay of Bengal and the SCS continued until autumn and spring of the following year. This shows possible remote connections between climate events related to the tropical Indian Ocean and variations in precipitation over EA.

This study investigated the effects of El Niño events on tropical cyclone (TC) characteristics over the western North Pacific (WNP) region. First, TC characteristics associated with large-scale atmospheric phenomena (i.e., genesis position, frequency, track, intensity, and duration) were investigated in the WNP in relation to various types of El Niño events—moderate central Pacific (MCP), moderate eastern Pacific (MEP), and strong basin-wide (SBW). Subsequently, the seasonal and regional variability of TC-induced rainfall across China was analyzed to compare precipitation patterns under the three El Niño types. When extreme El Niño events of varying degrees occurred, the local rainfall varied during the developmental and decaying years. The development of MEP and SBW was associated with a distinct change in TC-induced rainfall. During MEP development, TC-induced rainfall occurred in eastern and northeastern China, whereas in SBW, TC-induced heavy rainfall occurred in southwest China. During SBW development, the southwestern region was affected by TCs over a long period, with the eastern and northeastern regions being affected significantly fewer days. During El Niño decay, coastal areas were relatively more affected by TCs during MCP events, and the Pearl River basin was more affected during SBW events. This study’s results could help mitigate TC-related disasters and improve water-supply management.

This study analyzed the sensitivity of rainfall patterns in South China and the Indochina Peninsula (ICP) using statistical simulations of observational data. Quantitative changes in rainfall patterns over the ICP were examined for both wet and dry seasons to identify hotspots sensitive to ocean warming in the Indo-Pacific sector. The rainfall variability was amplified by combined and/or independent effects of the El Niño–Southern Oscillation and the Indian Ocean Dipole (IOD). During the years of El Niño and a positive phase of the IOD, rainfall is less than usual in Thailand, Cambodia, southern Laos, and Vietnam. Conversely, during the years of La Niña and a negative phase of the IOD, rainfall throughout the ICP is above normal, except in parts of central Laos, northern Vietnam, and South China. This study also simulated the change of ICP rainfall in the wet and dry seasons with intentional IOD changes and verified IOD-sensitive hotspots through quantitative analysis. The results of this study provide a clear understanding both of the sensitivity of regional precipitation to the IOD and of the potential future impact of statistical changes regarding the IOD in terms of understanding regional impacts associated with precipitation in changing climates.

In this study, an estimation of the future probable rainfall in Seoul, Korea, was performed, using non-stationary frequency analysis according to climate change and it was compared with the current probable rainfall. Hourly rainfall data provided by the Korea Meteorological Administration with durations of 1, 2, 3, 6, 12, 24, and 48-h were used as input. For the future projection of precipitation, the RCP 8.5 scenario was selected with the same durations. Moreover, the future hourly rainfall was extracted from using the daily precipitation from 29 Global Climate Models (GCMs), based on the statistical temporal down-scaling method and their corresponding bias corrections. Subsequently, the annual maximum precipitation was extracted for each year. In this study, both stationary and non-stationary frequency analysis was applied based on the observed and predicted time series data sets. In particular, for the non-stationary frequency analysis, the Differential Evolution Markov Chain technique, which combines the Bayesian-based Differential Evolution and Markov chain Monte Carlo methods, was adopted. Finally, the current and future intensity-duration-frequency curves were derived from the optimal probability distribution, and each probable rainfall was estimated. The results of the 29-scenario are presented with quantile estimations. The non-stationary frequency analysis results for Seoul revealed rainfalls of 94.4 mm/h for 30 y, 101.7 mm/h for 50 y, and 111.5 mm/h for 100 y return periods. The average value of the 29-GCM model ensemble was estimated to be approximately 5 mm/h higher than that obtained from the stationary frequency analysis. Considering the changes in hydrological characteristics due to climate change in Seoul, the results of this study could be utilized to pro-actively respond to natural disasters due to such phenomena.

This study analyzed temporal and regional responses of precipitation to the Indian Ocean Dipole (IOD) over southern China and the differences between IOD-only and El Niño–southern oscillation–IOD cases. The Mann–Kendall test and intentionally biased bootstrapping were used. The results revealed three main phases (development and peak, decay, and aftermath) of percentage changes in seasonal total rainfall and showed the most positive sensitivity to positive IOD events in southern China. Moreover, El Niño played an essential role in intensifying the positive response to positive IOD events in the first and second phases while contributing little to the third. In terms of precipitation variability (frequency, intensity, and magnitude), seasonal maximum 1-day precipitation and maximum number of consecutive dry days were more sensitive to positive IOD events than the maximum number of consecutive wet days and simple daily precipitation intensity index. This study enhances knowledge of the temporal and spatial sensitivity of precipitation features to positive IOD events over southern China.

This study analyzed the sensitivity of rainfall patterns over the Indochina Peninsula (ICP) to sea surface temperature in the Indian Ocean based on statistical simulations of observational data. Quantitative changes in rainfall patterns over the ICP were examined for both wet and dry seasons to identify hotspots sensitive to ocean warming in the Indo-Pacific sector. Rainfall variability across the ICP was confirmed amplified by combined and/or independent effects of the El Niño–Southern Oscillation and the Indian Ocean Dipole (IOD). During the years of El Niño and a positive phase of the IOD, rainfall is less than usual in Thailand, Cambodia, southern Laos, and Vietnam. Conversely, during the years of La Niña and a negative phase of the IOD, rainfall throughout the ICP is above normal, except in parts of central Laos and northern Vietnam. This study also simulated the change of ICP rainfall in the wet and dry seasons according to intentional IOD changes, and IOD-sensitive hotspots were verified through quantitative analysis. The results of this study provide clear understanding both of the sensitivity of regional precipitation to the IOD and of the potential future impact of statistical changes regarding the IOD in terms of understanding regional impacts associated with precipitation in a changing climate.