• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.170-170
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• 2018

In some case studies, the heavy precipitation events and rapid cyclogenesis in the extratropics can be caused by moist and warm tropical air masses. Tropical Moisture Exports (TME) correspond to the meridional transport of moist air masses, primarily born in tropical oceanic areas, to higher latitudes; and are closely related to flood events, especially in the mid-latitudes. The TME for the region of interest is mostly estimated by the back tracking approach using Lagrangian Analysis Tools (LAGRANTO) from ECMWF Re-Analysis (ERA) data. In this study, we aim to estimate the TME that are related to rainfall in Korea. The major moisture sources of the TME that contribute to heavy rainfall and extreme floods in Korea are identified. The TME is found to have significant connection with extreme events in Korea such as heavy rainfall and extreme flood events. The results show the most of the moisture sources comes from the west Pacific during the warm half of the year and it contributes significantly to the annual TME and is linked to the East Asian monsoon.

• Journal of Wetlands Research
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• v.18 no.1
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• pp.76-83
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• 2016

In recent, the series of extreme storm events were occurred by those continuous typhoons and the severe flood damages due to the loss of life and the destruction of property were involved. In this study, we call Mega flood for the Extreme flood occurred by these successive storm events and so we can have a hypothetical Mega flood by assuming that a extreme event can be successively occurred with a certain time interval. Inter Event Time Definition (IETD) method was used to determine the time interval between continuous events in order to simulate Mega flood. Therefore, the continuous extreme rainfall events are determined with IETD then Mega flood is simulated by the consecutive events : (1) consecutive occurrence of two historical extreme events, (2) consecutive occurrence of two design events obtained by the frequency analysis based on the historical data. We have shown that Mega floods by continuous extreme rainfall events were increased by 6-17% when we compared to typical flood by a single event. We can expect that flood damage caused by Mega flood leads to much greater than damage driven by a single rainfall event. The second increase in the flood caused by heavy rain is not much compared to the first flood caused by heavy rain. But Continuous heavy rain brings the two times of flood damage. Therefore, flood damage caused by the virtual Mega flood of is judged to be very large. Here we used the hypothetical rainfall events which can occur Mega floods and this could be used for preparing for unexpected flood disaster by simulating Mega floods defined in this study.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.235-235
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• 2015

This study investigates the impact of event characteristics on runoff dynamics during extreme flood events observed in a $8.5km^2$ experimental watershed located in South Korea. The 37 most extreme flood events with event rainfall in excess of 50 mm were analysed using an event-based rainfall-runoff model; the Revitalised Flood Hydrograph (ReFH) routinely used for design flood estimation in the United Kingdom. The ReFH model was fitted to each event in turn, and links were investigated between each of the two model parameters controlling runoff production and response time, respectively, and event characteristics such as rainfall depth, duration, intensity and also antecedent soil moisture. The results show that the structure of the ReFH model can effectively accommodate any nonlinearity in runoff production, but that the linear unit hydrograph fails to adequately represent a reduction in watershed response time observed for the more extreme events. By linking the unit hydrograph shape directly to rainfall depth, the consequence of the observed nonlinearity in response time is to increase design peak flow by between 50% for a 10 year return period, and up to 80% when considering the probable maximum flood (PMF).

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.1
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• pp.39-51
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• 2016

Extreme floods occur more often recently as the frequency of extreme storm events increase due to the climate change. Because the extreme flood exceeding the design flood can cause large-scale disasters, it is important to predict and prepare for the future extreme flood. Flood flow is affected by two main factors; rainfall and land use. To predict the future extreme flood, both changes in rainfall due to the climate change and land use should be considered. The objective of this study was to simulate the future design flood in the Hwangguji river watershed, South Korea. The climate and land use change scenarios were derived from the representative concentration pathways (RCP) 4.5 and 8.5 scenarios. Conversion of land use and its effects (CLUE) and hydrologic modelling system (HEC-HMS) models were used to simulate the land use change and design flood, respectively. Design floods of 100-year and 200-year for 2040, 2070, and 2100 under the RCP4.5 and 8.5 scenarios were calculated and analyzed. The land use change simulation described that the urban area would increase, while forest would decrease from 2010 to 2100 for both the RCP4.5 and 8.5 scenarios. The overall changes in design floods from 2010 to 2100 were similar to those of probable rainfalls. However, the impact of land use change on design flood was negligible because the increase rate of probable rainfall was much larger than that of curve number (CN) and impervious area.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.147-147
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• 2022

In January 2021 heavy flood affected South Kalimantan with causing many casualties. The heavy rainfall is predicted to be generated due to the ENSO (El Nino-Southern Oscillation). The weak La-Nina mode appeared to generate more convective cloud above the warmed ocean and result in extreme rainfall with high anomaly compared to past historical rainfall event. Subsequently, the antecedent soil moisture distribution showed to have an important role in generating the flood response. Saturated flow and infiltration excess mainly contributed to the runoff generation due to the high moisture capacity. The hydro-meteorological processes in this event were deeply analyzed using the coupled atmospheric model of Weather Research and Forecasting (WRF) and the hydrological model extension (WRF-Hydro). The sensitivity analysis of the flood response to the SST anomaly and the soil moisture capacity also compared. Result showed that although SST and soil moisture are the main contributors, soil moisture have more significant contribution to the runoff generation despite of anomaly rainfall occurred. Model performance was validated using the Global Precipitation Measurement (GPM) and Soil Moisture Operational Products System (SMOPS) and performed reasonably well. The model was able to capture the hydro-meteorological process of atmosphere and hydrological feedbacks in the extreme weather event.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.37-53
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• 2024

In this study, flood records from 79 sites across Thailand were analyzed to estimate flood indices using the regional frequency analysis based on the L-moments method. Observation sites were grouped into homogeneous regions using k-means and Ward's clustering techniques. Among various distributions evaluated, the generalized extreme value distribution emerged as the most appropriate for certain regions. Regional growth curves were subsequently established for each delineated region. Furthermore, 20- and 100-year return values were derived to illustrate the recurrence intervals of maximum rainfall across Thailand. The predicted return values tend to increase at each site, which is associated with growth curves that could describe an increasing long-term predictive pattern. The findings of this study hold significant implications for water management strategies and the design of flood mitigation structures in the country.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.164-164
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• 2023

This study investigates the importance of flood-influencing factors on the accuracy of flood risk mapping using the integration of remote sensing-based and machine learning techniques. Here, the Extreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms integrated with GIS-based techniques were considered to develop and generate flood risk maps. For the study area of NAPA County in the United States, rainfall data from the 12 stations, Sentinel-1 SAR, and Sentinel-2 optical images were applied to extract 13 flood-influencing factors including altitude, aspect, slope, topographic wetness index, normalized difference vegetation index, stream power index, sediment transport index, land use/land cover, terrain roughness index, distance from the river, soil, rainfall, and geology. These 13 raster maps were used as input data for the XGBoost and RF algorithms for modeling flood-prone areas using ArcGIS, Python, and R. As results, it indicates that XGBoost showed better performance than RF in modeling flood-prone areas with an ROC of 97.45%, Kappa of 93.65%, and accuracy score of 96.83% compared to RF's 82.21%, 70.54%, and 88%, respectively. In conclusion, XGBoost is more efficient than RF for flood risk mapping and can be potentially utilized for flood mitigation strategies. It should be noted that all flood influencing factors had a positive effect, but altitude, slope, and rainfall were the most influential features in modeling flood risk maps using XGBoost.

• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.335-346
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• 2016

This study produced the parameter maps of the Modified Bartlett-Lewis Rectangular Pulse (MBLRP) stochastic rainfall generation model across South Korea and developed and validated the web application that automates the process of rainfall generation based on the produced parameter maps. To achieve this purpose, three deferent sets of parameters of the MBLRP model were estimated at 62 ground gage locations in South Korea depending on the distinct purpose of the synthetic rainfall time series to be used in hydrologic modeling (i.e. flood modeling, runoff modeling, and general purpose). The estimated parameters were spatially interpolated using the Ordinary Kriging method to produce the parameter maps across South Korea. Then, a web application has been developed to automate the process of synthetic rainfall generation based on the parameter maps. For validation, the synthetic rainfall time series has been created using the web application and then various rainfall statistics including mean, variance, autocorrelation, probability of zero rainfall, extreme rainfall, extreme flood, and runoff depth were calculated, then these values were compared to the ones based on the observed rainfall time series. The mean, variance, autocorrelation, and probability of zero rainfall of the synthetic rainfall were similar to the ones of the observed rainfall while the extreme rainfall and extreme flood value were smaller than the ones derived from the observed rainfall by the degree of 16%-40%. Lastly, the web application developed in this study automates the entire process of synthetic rainfall generation, so we expect the application to be used in a variety of hydrologic analysis needing rainfall data.

• Journal of Korea Water Resources Association
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• v.49 no.8
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• pp.719-729
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• 2016

Extreme events of rainfall has increased mainly from climate change, resulting in more severe floods intensified by land use development. Appropriate estimation of design floods gets more attention to ensuring the safety of life and property in flood-prone areas for hydraulic structures such as dams and levees. In the current study, we reestimated the design flood of the Nam River Dam to adopt the influence of climatic change of hydrometeorological variables including recent datasets of extreme rainfall events. The climate change scenarios of extreme rainfall events in hourly scale that has been downscaled was used in analyzing the annual maximum rainfall for the weather stations in the Nam River Dam basin. The estimates of 200-year and 10,000-year return periods were calculated to provide a design flood and a probable maximum flood case for the Nam River Dam. The results present that the new estimate employing the RCP4.5 and RCP8.5 downscaled data is much higher than the original design flood estimated at the dam construction stage using a 200-year return period. We can conclude that the current dam area might be highly vulnerable and need an enhancement of the dam safety regarding the reduction of damage in Sachen bay from the outflow of Nam River Dam.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.16-16
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• 2011

Statistics of extreme rainfall play a vital role in engineering practice from the perspective of mitigation and protection of infrastructure and human life from flooding. While flood frequency assessments, based on river flood flow data are preferred, the analysis of rainfall data is often more convenient due to the finer spatial nature of rainfall recording networks, often with longer records, and potentially more easily transferable from site to site. The rainfall frequency analysis as a design tool has developed over the years in New Zealand from Seelye's daily rainfall frequency maps in 1947 to Thompson's web based tool in 2010. This paper will present a history of the development of New Zealand rainfall frequency analysis methods, and the details of the latest method, so that comparisons may in future be made with the development of Korean methods. One of the main findings in the development of methods was new knowledge on the distribution of New Zealand rainfall extremes. The High Intensity Rainfall Design System (HIRDS.V3) method (Thompson, 2011) is based upon a regional rainfall frequency analysis with the following assumptions: $\bullet$ An "index flood" rainfall regional frequency method, using the median annual maximum rainfall as the indexing variable. $\bullet$ A regional dimensionless growth curve based on the Generalised Extreme Value (GEV), and using goodness of fit test for the GEV, Gumbel (EV1), and Generalised Logistic (GLO) distributions. $\bullet$ Mapping of median annual maximum rainfall and parameters of the regional growth curves, using thin-plate smoothing splines, a $2km\times2km$ grid, L moments statistics, 10 durations from 10 minutes to 72 hours, and a maximum Average Recurrence Interval of 100 years.