• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.229-232
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• 2002

This study was conducted to estimate the design flood by the determination of best fitting order of LH-moments of the annual maximum series at six and nine watersheds in Korea and Australia, respectively. Adequacy for flood flow data was confirmed by the tests of independence, homogeneity, and outliers. Gumbel (GUM), Generalized Extreme Value (GEV), Generalized Pareto (GPA), and Generalized Logistic (GLO) distributions were applied to get the best fitting frequency distribution for flood flow data. Theoretical bases of L, L1, L2, L3 and L4-moments were derived to estimate the parameters of 4 distributions. L, L1, L2, L3 and L4-moment ratio diagrams (LH-moments ratio diagram) were developed in this study.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1441-1444
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• 2007

It is now widely acknowledged that climate variability modifies the frequency spectrum of hydrological extreme events. Traditional hydrological frequency analysis methodologies are not devised to account for nonstationarity that arises due to variation in exogenous factors of the causal structure. We use Hierarchical Bayesian Analysis to consider the exogenous factors that can influence on the frequency of extreme floods. The sea surface temperatures, predicted GCM precipitation, climate indices and snow pack are considered as potential predictors of flood risk. The parameters of the model are estimated using a Markov Chain Monte Carlo (MCMC) algorithm. The predictors are compared in terms of the resulting posterior distributions of the parameters associated with estimated flood frequency distributions.

• 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.44 no.10
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• pp.787-795
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• 2011

Extreme water events such as heavy rainfalls due to recent climate change are continually increasing and their scale has also shown an increasing trend. To overcome these natural disasters, this policy study suggests securing lateral river space as an effective method for extreme flood. To support the importance of restoration and expansion of lateral river space, Gumi upstream region of the Nakdong River basin was chosen as a target area and flood reduction analysis of the washland by using LISFLOOD model have been examined. The 500-year frequency flood was simulated for the estimation of possibly occurable flood level and it turns out that the secured lateral river space on the selected site effectively lowers about 0.53 m flood level and reduces the flood damage of the city on the lower reaches of the river. In addition, based on this result, multilateral river space securing plans were compared, and conservation easement and natural disaster insurance were suggested for sustainable and cost-effective alternatives. The costs of land purchase and conservation easement for securing the river space were also compared.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.741-745
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• 2007

The magnitude of natural disaster is much bigger than the past. Only short time return period can be estimated due to limited number of measured data. Therefore, back-data extension studies are undergoing in various area through historical records. In this study, data gathering and analysis of historical flood records such as Joseon wangjo sillok(Annals of Joseon Dynasty) and Jeungbo munheon bigo (enlarged encyclopedic literature) was achieved for the usage of extreme flood study in various ways. Analysis of 479 flood events from Joseon wangjo sillok and 143 flood events from Jeungbo munheon bigo during Joseon Dynasty was conducted in statistical way.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.127-136
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• 2024

Flooding presents a significant threat to infrastructure, and climate change is exacerbating these risks. High-speed rail (HSR) infrastructure, designed based on historical data, may struggle to cope with future extreme flood events. Infrastructure stakeholders require forecasting capabilities to predict the intensity and frequency of future floods so they can develop adaptive strategies to mitigate flood risks and impacts. Floods can cause significant damage to HSR infrastructure networks, disrupting their operations. Traditional network theory-based frameworks are insufficient for analyzing the three-dimensional effects of floods on HSR networks. To address this issue, this study proposes a comprehensive approach to assess flood risk and vulnerability under future climate scenarios for HSR networks. The method consists of three components. (i) Generate flood inundation data by utilizing global climate models, Shared Socioeconomic Pathways(SSPs), and the CaMa-Flood model. (ii) Fit extreme flood depths to the Gumbel distribution to generate flood inundation scenarios. (iii) Overlay flood scenarios on the HSR network and quantitatively assess network vulnerability based on topology network. When applied to the HSR system in mainland China, the results indicate that flood severity does not necessarily increase under higher SSPs, but may worsen over time. The minimum flood return period that causes HSR disruptions is decreasing, with Hubei Province showing a significant increase in HSR segment failure probability. Discontinuous phase transitions in HSR network topology metrics suggest potential nationwide collapses under future infrequent floods. These findings can inform preventive measures for the HSR sector and flood-resistant standards for HSR infrastructure. The method used in this study can be extended to analyze the vulnerability of other transportation systems to natural disasters, serving as a quantitative tool for improving resilience in a changing climate.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.5
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• pp.627-637
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• 2022

To know the magnitudes of extreme floods for various sizes of watersheds, massive streamflow data is fundamentally required. However, small/medium-size watersheds missed streamflow data because of the lack of gauge stations. In this study, the Streamflow Propagation Method (SPM) was applied to generate streamflow data for small/medium size watersheds with no measurements. Based on the generated streamflow data for ungauged watersheds at three different locations (i.e., Chungju Dam (CJD), Seomjin Dam (SJD), and Andong Dam (ADD) watersheds), the scale ranges of extreme floods were evaluated for different sizes of ungauged watersheds by using the specific flood distribution analysis. As a general result, a range of specific floods decreases with increasing watershed size. The distribution of the specific flood in the same size of a watershed possibly depends on the size and topography of the watershed area. The delivered equations were compared to show the relations between the specific flood and sizes of watersheds. In the comparisons of equations, the Creager envelope curve has the higher potential to represent the maximum flood distribution for each watershed. For the generalization of the maximum flood distribution for three watersheds, optimized envelop curves are obtained with lower RMSE than that of Creager envelope curve.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.23-30
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• 2021

In recent years, hazardous flash flooding has caused deaths and damage to infrastructure in Saudi Arabia. In this paper, our aim is to assess patterns and trends in climate means and extremes affecting flash flood hazards and water resources in Saudi Arabia for the purpose to improve risk assessment for forecast capacity. We would like to examine temperature, precipitation climatology and trend magnitudes at surface stations in Saudi Arabia. Based on the assessment climate patterns maps and trends are accurately used to identify synoptic situations and tele-connections associated with flash flood risk. We also study local and regional changes in hydro-meteorological extremes over recent decades through new applications of statistical methods to weather station data and remote sensing based precipitation products; and develop remote sensing based high-resolution precipitation products that can aid to develop flash flood guidance system for the flood-prone areas. A dataset of extreme events has been developed using the multi-decadal station data, the statistical analysis has been performed to identify tele-connection indices, pressure and sea surface temperature patterns most predictive to heavy rainfall. It has been combined with time trends in extreme value occurrence to improve the potential for predicting and rapidly detecting storms. A methodology and algorithms has been developed for providing a well-calibrated precipitation product that can be used in the early warning systems for elevated risk of floods.

• 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.