• Journal of Wetlands Research
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• v.9 no.2
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• pp.1-8
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• 2007

Since 2000, environmental policies and regulations in Korea are rapidly changing to TMDL(Total Maximum Daily Load) and nonpoint source control. This is due to bad water quality in drinking water sources. Although many environmental facilities having high removal efficiency are constructed and applied in nationwide for controling various pollutants from wastewaters, the water quality in rivers is worse and worse because of nonpoint pollution. In fact, TMDL is a new environmental regulation controling total daily loadings from watershed areas. Actually, the nonpoint pollutant is originated from various landuses and its control is based on TMDL regulation. Therefore, this research is performed to determine the size of detention basin to control nonpoint pollutants from resort developing areas. The detention basin is one of best management practices, which is useful for controling pollutants and flooding from the developing areas. However, it should be designed and constructed with cost effective method. Recent 10 years rainfall data are used to determine the size of detention basin. The cost effective size is determined to 7.4mm accumulated rainfall.

• Journal of Korea Water Resources Association
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• v.40 no.6 s.179
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• pp.447-458
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• 2007

In this study, the coastal urban flood prediction and warning system based on HEC-RAS and SWMM were investigated to evaluate a watershed of On-Cheon stream in Busan which has characteristics of costal area cased by flooding of coastal urban areas. The basis of this study is a selection of various geological data from the numerical map that is a watershed of On-Cheon stream and computation of hydrologic GIS data. Thiessen method was used for analyzing of rainfall on the On-Cheon stream and 6th regression equation, which is Huff's Type II was time-distribution of rainfall. To evaluate the deployment of flood prediction and warning system, risk depth was used on the 3 selected areas. To find the threshold runoff for hydraulic analysis of stream, HEC-RAS was used and flood depth and threshold runoff was considered with the effect of tidal water level. To estimate urban flash flood trigger rainfall, PCSWMM 2002 was introduced for hydrologic analysis. Consequently, not only were the criteria of coastal urban flood prediction and warning system decided on the watershed of On-Cheon stream, but also the deployment flow charts of flood prediction and warning system and operation system was evaluated. This study indicates the criteria of flood prediction and warning system on the coastal areas and modeling methods with application of ArcView GIS, HEC-RAS and SWMM on the basin. For the future, flood prediction and warning system should be considered and developed to various basin cases to reduce natural flood disasters in coastal urban area.

• Korean Journal of Weed Science
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• v.18 no.2
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• pp.106-115
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• 1998

This study was conducted to investigate the effect of oxadiazon, molinate, thiobencarb before seeding on control of red rice and barnyardgrass in water-seeded rice. High application rate plot among oxadiazon treatment plots could observe phytotoxicity symptoms depending on field conditions, but these injury recovered gradually with time. Molinate and thiobencarb application plots at the concentration of 225~400, 210~420g ai/l0a respectively were not observed phytotoxicity. Control of red rice was different according to kinds of herbicides and application rates. Oxadiazon showed higher control performance at the concentration of more than 60g ai/10a. Control effect of molinate and thiobencarb against red rice was enhanced with the increase of application rate, and both herbicides showed satisfactory effect at more than 300g ai/10a. Control of barnyardgrass showed up to 90~100% in all tested herbicides. There was no significant yield reduction by oxadiazon, molinate, and thiobencarb application before seeding in all tested field. In the pot experiment, crop injury, seedling stand, and early growth were more advantageous at time of drainge after one day after seeding than flooding until rooting.

• Journal of Korea Water Resources Association
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• v.52 no.10
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• pp.729-742
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• 2019

Natural disasters such as floods has been increased in many parts of the world, also Korea is no exception. The biggest part of natural damage in South Korea was caused by the flooding during the rainy season in every summer. The existing flood vulnerability analysis cannot explain the reality because of the repeated changes in topography. Therefore, it is necessary to calculate a new flood vulnerability index in accordance with the changed terrain and socio-economic environment. The priority of the investment for the flood prevention and mitigation has to be determined using the new flood vulnerability index. Total 25 urban districts in Seoul were selected as the study area. Flood vulnerability factors were developed using Pressure-State-Response (PSR) structures. The Pressure Index (PI) includes nine factors such as population density and number of vehicles, and so on. Four factors such as damage of public facilities, etc. for the Status Index (SI) were selected. Finally, seven factors for Response Index (RI) were selected such as the number of evacuation facilities and financial independence, etc. The weights of factors were calculated using AHP method and Fuzzy AHP to implement the uncertainties in the decision making process. As a result, PI and RI were changed, but the ranks in PI and RI were not be changed significantly. However, SI were changed significanlty in terms of the weight method. Flood vulnerability index using Fuzzy AHP shows less vulnerability index in Southern part of Han river. This would be the reason that cost of flood mitigation, number of government workers and Financial self-reliance are high.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.347-354
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• 2021

In the past few years, various damages have occurred in the vicinity of rivers due to flooding. In order to alleviate such flood damage, structural and non-structural measures are being established, and one of the important non-structural measures is to establish a flood warning system. In general, in order to establish a flood warning system, the water level of the flood alarm reference point is set, the critical flow corresponding thereto is calculated, and the warning precipitation amount corresponding to the critical flow is calculated through the Geomorphological Instantaneous Unit Hydrograph (GIUH) rainfall-runoff model. In particular, when calculating the critical flow, various studies have calculated the critical flow through the Manning formula. To compare the adequacy of this, in this study, the critical flow was calculated through the HEC-RAS model and compared with the value obtained from Manning's equation. As a result of the comparison, it was confirmed that the critical flow calculated by the Manning equation adopted excessive alarm precipitation values and lead a very high flow compared to the existing design precipitation. In contrast, the critical flow of HEC-RAS presented an appropriate alarm precipitation value and was found to be appropriate to the annual average alarm standard. From the results of this study, it seems more appropriate to calculate the critical flow through HEC-RAS, rather than through the existing Manning equation, in a situation where various river projects have been conducted resulting that most of the rivers have been surveyed.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.327-335
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• 2020

The method of selecting an existing flood hazard area via a numerical model requires considerable time and effort. In this regard, this study proposes a method for selecting flood vulnerable areas through topographic analysis based on a surface runoff mechanism to reduce the time and effort required. Flood vulnerable areas based on runoff mechanisms refer to those areas that are advantageous in terms of the flow accumulation characteristics of rainfall-runoff water at the surface, and they generally include lowlands, mild slopes, and rivers. For the analysis, a digital topographic map of the target area (Seoul) was employed. In addition, in the topographic analysis, eight topographic factors were considered, namely, the elevation, slope, profile and plan curvature, topographic wetness index (TWI), stream power index, and the distances from rivers and manholes. Moreover, receiver operating characteristic analysis was conducted between the topographic factors and actual inundation trace data. The results revealed that four topographic factors, namely, elevation, slope, TWI, and distance from manholes, explained the flooded area well. Thus, when a flood vulnerable area is selected, the prioritization method for various factors as proposed in this study can simplify the topographical analytical factors that contribute to flooding.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.6
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• pp.51-64
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• 2022

Along with the urbanization, the risk of urban flooding due to climate change is increasing. Flood regulation, one of the ecosystem services, is implemented in the different level of function of flood risk mitigation by the type of ecosystem such as forests, arable land, wetlands etc. Land use changes due to development pressures have become an important factor in increasing the vulnerability by flash flood. This study has conducted evaluating the urban flood regulation service using InVEST UFRM(Urban Flood Risk Model). As a result of the simulation, the potential water retention by ecosystem type in the event of a flash flood according to RCP 4.5(10 year frequency) scenario was 1,569,611 tons in urbanized/dried areas, 907,706 tons in agricultural areas, 1,496,105 tons in forested areas, 831,705 tons in grasslands, 1,021,742 tons in wetlands, and 206,709 tons in bare areas, the water bodies was estimated to be 38,087 tons. In the case of more severe 100-year rainfall, 1,808,376 tons in urbanized/dried areas, 1,172,505 tons in agricultural areas, 2,076,019 tons in forests, 1,021,742 tons in grasslands, 47,603 tons in wetlands, 238,363 tons in bare lands, and 52,985 tons in water bodies. The potential economic damage from flood runoff(100 years frequency) is 122,512,524 thousand won in residential areas, 512,382,410 thousand won in commercial areas, 50,414,646 thousand won in industrial areas, 2,927,508 thousand won in Infrastructure(road), 8,907 thousand won in agriculture, Total of assuming a runoff of 50 mm(100 year frequency) was estimated at 688,245,997 thousand won. In a conclusion. these results provided an overview of ecosystem functions and services in terms of flood control, and indirectly demonstrated the possibility of using the model as a tool for policy decision-making. Nevertheless, in future research, related issues such as application of models according to various spatial scales, verification of difference in result values due to differences in spatial resolution, improvement of CN(Curved Number) suitable for the research site conditions based on actual data, and development of flood damage factors suitable for domestic condition for the calculation of economic loss.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.575-586
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• 2023

River vegetation has important functions such as providing a habitat for the river ecosystem and physical stability of the river bank. It also has adverse effects such as aggravating flood damages due to the increase in roughness coefficient and drag forces. River vegetation management is very important in finding a balance between flood and ecological management. There are still many uncertainties about the effect of vegetation on rivers. In this study, in order to analyze the effect of vegetated flow, the flow patterns according to the vegetation roughness are analyzed through a two-dimensional unsteady flow model for Chopyeong island of the Imjin River. According to the results of the 2D flow analysis using the HEC-RAS 2D model, the velocity distribution in the bend of the Imjin River was greatly affected by the vegetation roughness of Chopyeong Island. The formation of the main flow outside the bend of Chopyeong Island during flooding is presumed due to the influence of tree and grass on Chopyeong Island. If tree are distributed throughout Chopyeong Island, the velocity outside the bend is expected to be higher. River vegetation causes the effect of raising the water level, and could cause a change in the velocity distribution.

• Journal of Ecology and Environment
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• v.38 no.4
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• pp.405-413
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• 2015

We classified the streamside plant community by phyto-sociological method and analyzed the relationship between environment factors and vegetation structure by using the classification and ordination method. We found that twenty one plant communities were classified according to dominant species at the natural streamside valley forest with surveying the 65 quadrats (10 m × 10 m). From the survey results, the hardwood plant communities were classified as streamside valley forest and the softwood plant communities as riparian forest according to the degree of flooding. The valley forest had a distribution of 17 plant communities which was 65% (42 quadrats) of 65 quadrats: Maackia amurensis community, Betula davurica community, Quercus variabilis community, Pinus densiflora community, Q. serrata community, Prunus sargentii community, and Meliosma oldhamii community etc. The riparian forest had a distribution of four plant communities which was 35% (23 quadrats) of 65 quadrats: Salix koreensis community, S. rorida community, S. purpurea var. japonica community, and S. glandulosa community, etc. From the two-way indicator species analysis (TWINSPAN) analysis, we found indicator species Oplismenus undulatifolius and Lindera obtusiloba for the streamside valley forest and Humulus japonicus, Phragmites japonica, and S. koreensis for the riparian forest. From the results of the canonical correspondence analysis (CCA), coordinates, altitude, and stream structure showed low correlation to the distribution of the plant community. Therefore, it seemed that valley forest and riparian forest were distinguished by the stream gradient and waterway width which determined by the stream water level.