• Journal of Korean Society of Environmental Engineers
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• v.34 no.11
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• pp.720-728
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• 2012

In general, nonpoint pollutant of a watershed is drained out in the form of storm water runoff during rainfall events. As the bulk of the nonpoint pollutant is in adsorbed form on particulate matters, in order to understand the behavior of nonpoint pollutant it is essential to grasp the characteristics of particulate matters in rainfall runoff. Though, previous studies for the relationship between the runoff characteristics of pollutants and the size distribution of particulate matters are very rare. In this study, a small non-urbanized area (basin area of 52.8 ha) with various landuse types including paddy, dry fields and forest was selected and investigated in detail for the runoff properties of each pollutant during several rainfall events. The correlation and effects between particulate matters and nonpoint pollutant were analyzed quantitatively. As a result, the significant first flush was observed on each event and it became clear that fine particulate matters ($80{\mu}m$ or less) has contributed in the runoff process of nutrients and heavy metals. Organic matters ($BOD_5$, TOC), nutrients (TN, TP) and several heavy metals (Al, Cr, Cu, Fe, Hg and Zn) represented high correlations with SS (total), VSS, SS (d < $20{\mu}m$) and SS ($20{\mu}m$ $$\leq_-$$ d < $80{\mu}m$). On the other hand, $COD_{cr}$, Cd, Mn and Pb did not show clear correlations with the behavior of particulate matters. Therefore, we have to examine the introduction of nonpoint pollution mitigation facilities considering the facts that nonpoint pollutant runoff process has high correlation with the behavior of particulate matters and is changeable based on the target pollutants.

• Journal of Environmental Policy
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• v.12 no.3
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• pp.21-47
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• 2013

The aim is to analysis landslide vulnerability in Inje, Korea, using GCI(Geospatial Correlative Integration) and probability rainfalls based on geographic information system (GIS). In order to achieve this goal, identified indicators influencing landslides based on literature review. We include indicators of exposure to climate(rainfall probability), sensitivity(slope, aspect, curvature, geology, topography, soil drainage, soil material, soil thickness and soil texture) and adaptive capacity(timber diameter, timber type, timber density and timber age). All data were collected, processed, and compiled in a spatial database using GIS. Karisan-ri that had experienced 470 landslides by Typhoon Ewinia in 2006 was selected for analysis and verification. The 50% of landslide data were randomly selected to use as training data, while the other 50% being used for verification. The probability of landslides for target years (1 year, 3 years, 10 years, 50 years, and 100 years) was calculated assuming that landslides are triggered by 3-day cumulative rainfalls of 449 mm. Results show that number of slope has comparatively strong influence on landslide damage. And inclination of $25{\sim}30^{\circ}C$, the highest correlation landslide. Improved previous landslide vulnerability methodology by adopting GCI. Also, vulnerability map provides meaningful information for decision makers regarding priority areas for implementing landslide mitigation policies.

• Economic and Environmental Geology
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• v.54 no.4
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• pp.409-425
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• 2021

Carbon dioxide has been known to be a typical greenhouse gas causing global warming, and a number of efforts have been proposed to reduce its concentration in the atmosphere. Among them, carbon dioxide capture and storage (CCS) has been taken into great account to accomplish the target reduction of carbon dioxide. In order to commercialize the CCS, its safety should be secured. In particular, if the stored carbon dioxide is leaked in the arable land, serious problems could come up in terms of crop growth. This study was conducted to investigate the effect of carbon dioxide leaked from storage sites on soil fertility. The leakage of carbon dioxide was simulated using the facility of its artificial injection into soils in the laboratory. Several soil chemical properties, such as pH, cation exchange capacity, electrical conductivity, the concentrations of exchangeable cations, nitrogen (N) (total-N, nitrate-N, and ammonia-N), phosphorus (P) (total-P and available-P), sulfur (S) (total-S and available-S), available-boron (B), and the contents of soil organic matter, were monitored as indicators of soil fertility during the period of artificial injection of carbon dioxide. Two kinds of soils, such as non-cultivated and cultivated soils, were compared in the artificial injection tests, and the latter included maize- and soybean-cultivated soils. The non-cultivated soil (NCS) was sandy soil of 42.6% porosity, the maize-cultivated soil (MCS) and soybean-cultivated soil (SCS) were loamy sand having 46.8% and 48.0% of porosities, respectively. The artificial injection facility had six columns: one was for the control without carbon dioxide injection, and the other five columns were used for the injections tests. Total injection periods for NCS and MCS/SCS were 60 and 70 days, respectively, and artificial rainfall events were simulated using one pore volume after the 12-day injection for the NCS and the 14-day injection for the MCS/SCS. After each rainfall event, the soil fertility indicators were measured for soil and leachate solution, and they were compared before and after the injection of carbon dioxide. The results indicate that the residual concentrations of exchangeable cations, total-N, total-P, the content of soil organic matter, and electrical conductivity were not likely to be affected by the injection of carbon dioxide. However, the residual concentrations of nitrate-N, ammonia-N, available-P, available-S, and available-B tended to decrease after the carbon dioxide injection, indicating that soil fertility might be reduced. Meanwhile, soil pH did not seem to be influenced due to the buffering capacity of soils, but it is speculated that a long-term leakage of carbon dioxide might bring about soil acidification.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.981-992
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• 2023

Due to recent global climate change, the scale of flood damage is increasing as rainfall is concentrated and its intensity increases. Rain on a scale that has not been observed in the past may fall, and long-term rainy seasons that have not been recorded may occur. These damages are also concentrated in ASEAN countries, and many people in ASEAN countries are affected, along with frequent occurrences of flooding due to typhoons and torrential rains. In particular, the Bandung region which is located in the Upper Chitarum River basin in Indonesia has topographical characteristics in the form of a basin, making it very vulnerable to flooding. Accordingly, through the Official Development Assistance (ODA), a flood forecasting and warning system was established for the Upper Citarium River basin in 2017 and is currently in operation. Nevertheless, the Upper Citarium River basin is still exposed to the risk of human and property damage in the event of a flood, so efforts to reduce damage through fast and accurate flood forecasting are continuously needed. Therefore, in this study an artificial intelligence-based river flood water level forecasting model for Dayeu Kolot as a target station was developed by using 10-minute hydrological data from 4 rainfall stations and 1 water level station. Using 10-minute hydrological observation data from 6 stations from January 2017 to January 2021, learning, verification, and testing were performed for lead time such as 0.5, 1, 2, 3, 4, 5 and 6 hour and LSTM was applied as an artificial intelligence algorithm. As a result of the study, good results were shown in model fit and error for all lead times, and as a result of reviewing the prediction accuracy according to the learning dataset conditions, it is expected to be used to build an efficient artificial intelligence-based model as it secures prediction accuracy similar to that of using all observation stations even when there are few reference stations.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.69-79
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• 2013

This paper presents a two-stage geostatistical integration approach that aims at downscaling of coarse scale remote sensing data. First, downscaling of the coarse scale sedoncary data is implemented using area-to-point kriging, and this result will be used as trend components on the next integration stage. Then simple kriging with local varying means that integrates sparse precise observation data with the downscaled data is applied to generate thematic information at a finer scale. The presented approach can not only account for the statistical relationships between precise observation and secondary data acquired at the different scales, but also to calibrate the errors in the secondary data through the integration with precise observation data. An experiment for precipitation mapping with weather station data and TRMM (Tropical Rainfall Measuring Mission) data acquired at a coarse scale is carried out to illustrate the applicability of the presented approach. From the experiment, the geostatistical downscaling approach applied in this paper could generate detailed thematic information at various finer target scales that reproduced the original TRMM precipitation values when upscaled. And the integration of the downscaled secondary information with precise observation data showed better prediction capability than that of a conventional univariate kriging algorithm. Thus, it is expected that the presented approach would be effectively used for downscaling of coarse scale data with various data acquired at different scales.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.11
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• pp.1072-1081
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• 2012

For Ka-band satellite communication system, a new flexible payload technologies which can compensate rain attenuation have to be developed. The Ka-band satellite output power control technology enables to adjust downlink output power of satellite payload in Ka-band (19.8 ~ 22.2 GHz). In this paper, we introduce multi-beam antenna with multi-port amplifiers for Ka-band flexible output power allocation system. We have designed multi-beam antenna with array-fed reflector to form 8 beams on the Korean Peninsula. The target EIRP per beam is more than 59 dBW. The system is designed to present 6 dB boost beams for rainfall areas. Individual beams were optimized by the excited amplitude and phase of feed elements of the feed cluster. The multi-port amplifier(MPA) is one of effective approaches for flexible power allocation in combination with multi-beam antenna. In case of using MPA in multi-beam system, the inter-port isolation characteristic of MPA is important parameter to avoid interference among the output ports. In this paper, we propose a new MPA structure that consists of two $4{\times}4$ Buttler matrixes and phase/amplitude controllable power amplifier modules.

• Journal of Korea Water Resources Association
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• v.43 no.10
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• pp.865-881
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• 2010

To evaluate influence of the future climate change on water environment, it is necessary to use a rainfall-runoff model, or a basin model allowing us to simultaneously simulate water quality factors such as sediment and nutrient material. Thus, SWAT is selected as a watershed-based model and Nakdong river basin is chosen as a target basin for this study. To apply climate change scenarios as input data to SWAT, Australian model (CSIRO: Mk3.0, CSMK) and Canadian models (CCCma: CGCM3-T47, CT47) of GCMs are used. Each GCMs which have A2, B1, and A1B scenarios effectively represent the climate characteristics of the Korean peninsula. For detecting climate change in Nakdong river basin, precipitation and temperature, increasing rate of these were analyzed in each scenarios. By simulation results, flow and increasing rate of these were analyzed at particular points which are important in the object basin. Flow and variation of flow in the scenarios for present and future climate changes were compared and analyzed by years, seasons, divided into mid terms. In most of the points temperature and flow rate are increased, because climate change is expected to have a significant effect on rising water temperature and flow rate of river and lake, further on the basis of this study result should set enhancing up water control project of hydraulic structures caused by increasing outer discharge of the Nakdong River Basin due to climate change.

• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.629-642
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• 2018

Future variability of the spatial patterns of rainfall events is the point of water-related risks and impacts of climate change. Recent related researches are mostly conducted based on the outcomes from General Circulation Models (GCMs), especially Coupled Model Intercomparison Project, phase 5 (CMIP5) GCMs which are the most advanced version of climate modeling system. GCM data have been widely used for various studies as the data utility keep getting improved. Meanwhile the model performances especially for raw GCM outputs are rarely evaluated prior to the applications although the process would essential for reasonable use of model forecasts. This study attempt to quantitatively evaluate the skills of 29 CMIP5 GCMs in reproducing spatial climatologies of precipitation in East Asia. We used 3 different gridded observational data as the references available over the study area and calculated correlation and errors of spatial patterns simulated by GCMs. As a result, the study presented diversity of the GCM evaluation in the performance, rank, or accuracy by different configurations, such as target area, evaluation method, and observation data. Yet, we found that Hadley-centre affiliated models comparatively performs better for the meso-scale area in East Asia and MPI_ESM_MR and CMCC family showed better performance specifically for the korean peninsula. We expect that the results and thoughts of this study would be considered in screening suitable GCMs for specific area, and finally contribute to extensive utilization of the results from climate change related researches.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.181-196
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• 2019

Floods can be caused by a variety of factors, and the main cause of floods is the exceeding of urban drainage system or river capacity. In addition, rainfall frequently occurs that causes large watershed runoff. Since the existing methodology of preparing for flood risk map is based on hydraulic and hydrological modeling, it is difficult to analyse for a large area because it takes a long time due to the extensive data collection and complex analysis process. In order to overcome this problem, this study proposes a methodology of mapping for flood vulnerable area that considered the surface runoff mechanism. This makes it possible to reduce the time and effort required to estimate flood vulnerabilities and enable detailed analysis of large areas. The target area is Seoul, and it was confirmed that flood damage is likely to occur near selected vulnerable areas by verifying using 2×2 confusion matrix and ROC curve. By selecting and prioritizing flood vulnerable areas through the surface runoff mechanism proposed in this study, the establishment of systematic disaster prevention measures and efficient budget allocation will be possible.

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.653-660
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• 2017

The SCS-CN (Soil Conservation Service-Curve Number) method has been practically applied for estimating the effective precipitation. The CN is used to be determined according to the land use condition based on the US standard. However, there are two distinctive differences between U.S. and Korean land use conditions: mountainous (forest) and rice paddy area that cover more than 70% of the Korean territory. The previous work proposed to use 79 for rice paddy area, regardless of the soil type. Because US SCS's goal was originally to increase crops, the SCS classification standard provides only for woods and there are no criteria to distinguish the wood and forest. To determine the CN for forest, alternatively the U.S. Forest Service criteria have been employed in practice considering hydrologic condition class. In this study, we investigated the change of the forest CN using the observed rainfall - runoff data within the target area. The results indicated that the CN for forest was suitable for HC=1, and the corresponding CNs were redetermined between 54 and 55.