• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.1
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• pp.175-182
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• 2018

In this study, flood quantiles were estimated at ungauged watersheds by adjusting the flood quantiles from the design rainfall - runoff analysis (DRRA) method based on regional frequency analysis. Comparing the flood frequency analysis (FFA) and DRRA, it was found that the flood quantiles estimated by the DRRA method were overestimated by 52%. In addition, a practical method was suggested to make an flood index using natural flows to apply the regional frequency analysis (RFA) to ungauged watersheds. Considering the relationships among DRRA, FFA, and RFA, we derived an adjusting formula that can be applied to estimate flood quantiles at ungauged watersheds. We also employed Leave-One-Out Cross-Validation scheme and skill score to verify the method proposed in this study. As a result, the proposed model increased the accuracy by 23.2% compared to the existing DRRA method.

• Journal of Korea Water Resources Association
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• v.43 no.3
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• pp.257-264
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• 2010

The amount and continuity of precipitation data used in a hydrological analysis may exert a big influence on the reliability of the analysis. It is a fundamental process to estimate the missing data caused by such as a breakdown of the rainfall recording machine or to expand a short period of rainfall data. In this study a linear programming method treated as a data-driven approach for estimating the missing rainfall data is compared with seven other methods widely used and its superiority is certified. The data used in this research are annual precipitation ones during 17 years at the Cheolwon station including an ungauged period of 15 years and its five surrounding stations. By use of this certified method the ungauged precipitation values at the Cheolweon station are estimated and the areal averages of annual precipitation data for 32 years at the Han River basin are calculated.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.781-796
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• 2019

This study attempted to simulate the flood discharge in the Duman River basin containing Hoeryong City and Musan County of North Korea where were damaged from Typhoon Lionrock on August, 2016. For hydrological modelling remotely sensed datasets were used to estimate watershed properties and hydrologic factors because the basin is ungauged where hydrological observation is not exist or sparse. For validation we applied our methodology and datasets to the Soyanggang Dam basin. It has not only similar shape factor and compactness ratio to those of the target basin but also accurate, adequate, and abundant measurements. The results showed that the flood discharge from Typhoon Lionrock corresponded to three to five years design floods in the Duman River basin. This indicate that the Duman River basin has a high risk of flood in the near future. Finally this study demonstrated that remotely sensed data and geographic information could be utilized to simulate flood discharge in an ungauged watershed.

• Journal of Korean Society on Water Environment
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• v.27 no.5
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• pp.670-679
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• 2011

Chlorophyll-a is a major water quality indicator for an algal bloom in streams and lakes. The purpose of the study is to estimate chlorophyll-a concentration in tributaries of the Seonakdonggang by an artificial neural network (ANN). As the tributaries are ungauged streams, a watershed runoff and quality model was used to simulate water quality parameters. The tributary watersheds include urban area and thus Storm Water Management Model (SWMM) was used to simulate TN, TP, BOD, COD, and SS. SWMM, however, can not simulate chlorophyll-a. The chlorophyll-a series data from the tributaries were estimated by the ANN and the simulation results of water quality parameters using SWMM. An assumption used is as follows: the relation between water quality parameters and chlorophyll-a in the tributaries of the Seonakdonggang would be similar to that in the mainstream of the Seonakdonggang. On the assumption, the measurement data of water quality and chlorophyll-a in the mainstream of the Seonakdonggang were used as the learning data of the ANN. Through the sensitivity analysis, the learning data combination of water quality parameters was determined. Finally, chlorophyll-a series were estimated for tributaries of the Seonakdonggang by the ANN and TN, TP, BOD, COD, and temperature data from those streams. The relative errors between the estimated and measured chlorophyll-a were approximately 40 ~ 50%. Though the errors are somewhat large, the estimation process for chlorophyll-a may be useful in ungauged streams.

• Journal of Korean Society on Water Environment
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• v.32 no.3
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• pp.310-317
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• 2016

It has been a big problem to estimate rainfall for the studies of mud-debris flows because the estimated rainfall from the nearest AWS (Automatic Weather Station) can tend to be quite inaccurate at individual sites. This study attempts to improve this problem through accurate rainfall depth estimation by applying an artificial neural network with radar rainfall data. For this, three models were made according to utilizing methodologies of rainfall data. The first model uses the nearest rainfall, observing the site from an ungauged site. The second uses only radar rainfall data and the third model integrates the above two models using both radar and observed rainfall at the sites around the ungauged site. This methodology was applied to the metropolitan area in Korea. It appeared as though the third model improved rainfall estimations by the largest margin. Therefore, the proposed methodology can be applied to forecast mud-debris flows in ungageed sites.

• Journal of Korea Water Resources Association
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• v.44 no.9
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• pp.721-730
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• 2011

The objective of this study is to assess the availability of streamflow simulation using Radar-AWS Rain rate (RAR) data which is produced by KMA on real-time. Chuncheon dam upstream basin is selected as study area and total area is 4859.73 $km^2$. Mean Areal Precipitation (MAP) using AWS and RAR are calculated on 5 subbasin. The correlationship of hourly MAPs between AWS and RAR is weak on ungauged subbasins but that is relatively high on gauged ones. We evaluated the simulated discharge using the MAPs derived from two data types during flood season from 2006 to 2009. The simulated discharges using AWS on Chuncheon dam (gauged basin) are well fitted with measured ones. In some cases, however, discharges using AWS on Hwacheon dam and Pyeonghwa dam with some ungauged subbasins are overestimated on the other hand, ones using RAR in the same case are well fitted with measured ones. The hourly RAR data is useful for the real-time river forecast on the ungauged basin in view of the results.

• Journal of Korea Water Resources Association
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• v.51 no.9
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• pp.769-782
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• 2018

Estimation of design floods is typically required for hydrologic design purpose. Design floods are routinely estimated for water resources planning, safety and risk of the existing water-related structures. However, the hydrologic data, especially streamflow data for the design purposes in South Korea are still very limited, and additionally the length of streamflow data is relatively short compared to the rainfall data. Therefore, this study collected a large number design flood data and watershed characteristics (e.g. area, slope and altitude) from the national river database. We further explored to formulate a scaling approach for the estimation of design flood, which is a function of the watershed characteristics. Then, this study adopted a Hierarchical Bayesian model for evaluating both parameters and their uncertainties in the regionalization approach, which models the hydrologic response of ungauged basins using regression relationships between watershed structure and model. The proposed modeling framework was validated through ungauged watersheds. The proposed approach have better performance in terms of correlation coefficient than the existing approach which is solely based on area as a predictor. Moreover, the proposed approach can provide uncertainty associated with the model parameters to better characterize design floods at ungauged watersheds.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1183-1192
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• 2020

Economic analysis is a basic step in establishing disaster mitigation measures, but it is difficult to verify the results due to uncertainty. Therefore, the scope of investigation and analysis is wide. However, it is difficult to predict the amount of damage caused by flooding because the collection of relevant data is limited in the ungauged basin. In this study, distributed runoff analysis and flooding analysis were performed, and a method of estimating the amount of flood damage in the ungauged basin was proposed using collectible social and economic indicators and flood analysis results. For distributed runoff analysis and flooding analysis, GRM (Grid based Rainfall-runoff Model) and G2D (Grid based 2-Dimensional land surface flood model) developed by Korea Institute of Civil engineering and Building Technology were used. The method of substituting collectible social and economic indicators into the simple method and improvement method was used to estimate the amount of flood damage. As a result of the study, it was possible to estimate the amount of flood damage using satellite data and social and economic indicators in the ungauged basin.

• Magazine of the Korean Society of Agricultural Engineers
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• v.57 no.1
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• pp.73-80
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• 2015

• Journal of Korean Society on Water Environment
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• v.25 no.2
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• pp.245-255
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• 2009

CN values are changed by various surface condition, which is cover type or treatment, hydrologic condition, or percent impervious area, even the same combination of land use and hydrologic soil group. In this study, CN parameters were regionalized for Nakdong River Basin by Long-Term Hydrologic Impact Assessment (L-THIA) coupled with SCE-UA, which is one of the global optimization technique. Six watersheds were selected for calibration (optimization) and periodic validation and two watersheds for spatical validation as ungauged watershed within Nakdong River Basin. Nash-Sutcliffe (NS) values were 0.66~0.86 for calibration, 0.68~0.91 for validation, and 0.60 and 0.85 for ungauged watersheds, respectively. Urban area for the selected watersheds covered high impervious area with 85% for residential area and 92% for commercial/industrial/transportation area. Hydrologic characteristics for crop area was similar to row crop with contoured treatment and poor hydrologic condition. For the forested area, hydrologic characteristics could be clearly distinguished from the leaf types of plant. Deciduous, coniferous, and mixed forest showed low, moderate, and high runoff rates by representing wood with fair and poor hydrologic condition, and wood-grass combination with fair hydrologic condition, respectively. CN parameters from this study could be strongly recommended to be used to simulate runoff for ungauged watershed.