• Journal of Korea Water Resources Association
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• v.31 no.5
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• pp.621-635
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• 1998

The feasibility of cloud seeding in Korea is presented from analyses of precipitation, cloud amount, satellite data, and upper air data. The daily mean precipitation over Dae-Kwan-Ryong is the largest(~4.5 mm/day), while the intensity of precipitation (amount of yearly rainfall divided by the frequency of rain days) over Southern area is above 14 mm/day, which shows the largest in Korea. Both the daily mean and the intensity of precipitation over Andong area are the smallest with values of ~2.7 mm/day and ~11 mm/day, respectively. In the meanwhile, the occurrence frequency of appropriate cloud top temperature (-10'~-30') for cloud seeding over the region has a large value (~130 days/year). The precipitation patterns of the region vary with wind direction and intensity calculated from 43 AWSs(Automatic Weather Station) and the additional 7 rain guages which were installed along Northern and Southern part of the Sobaek mountain. The Sc(Stratocumulus) cloud type over Andong is frequently observed, and Cirrus and Altostratus next. From the results, it is estimated that the feasibility of cloud seeding over the area would be high if a proper strategy of cloud seeding is set up. LCL (Lifting Condensation Level) and CCL (Convective Condensation Level) have the most frequency in 1000-950 hPa being occupied 4/9 of total analysis period and in 400-500 hPa, respectively, with both small variations from season to season. The correlation between vapor mixing ratio and CCL is the highest in Summer and the lowest in Winter. It means that the height of cumulus in Summer is high with an abundant water vapor but vice versa in Winter, and that the strategy of cloud seeding should be different with seasons.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.1
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• pp.45-55
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• 2017

Climate change causes changes in rainfall patterns, temperature and drought frequency. Climate change impact influences on water management and crop production. It is critical issue in agricultural industry. Rice is a staple cereal crop in South Korea and Korea uses a ponding system for its paddy fields which requires a significant amount of water. In addition, water supply has inter-relationship with crop production which indicates water productivity. Therefore, it is important to assess overall impacts of climate change on water resource and crop production. A water footprint concept is an indicator which shows relationship between water use and crop yield. In addition, it generally composed of three components depending on water resources: green, blue, grey water. This study analyzed the change trend of water footprint of paddy rice under the climate change. The downscaled climate data from HadGEM3-RA based on RCP 8.5 scenario was applied as future periods (2020s, 2050s, 2080s), and historical climate data was set to base line (1990s). Depending on agro-climatic zones, Suwon and Jeonju were selected for study area. A yield of paddy rice was simulated by using FAO-AquaCrop 5.0, which is a water-driven crop model. Model was calibrated by adjusting parameters and was validated by Mann-Whitney U test statistically. The means of water footprint were projected increase by 55 % (2020s), 51 % (2050s) and 48 % (2080s), respectively, from the baseline value of $767m^2/ton$ in Suwon. In case of Jeonju, total water footprint was projected to increase by 46 % (2020s), 45 % (2050s), 12 % (2080s), respectively, from the baseline value of $765m^2/ton$. The results are expected to be useful for paddy water management and operation of water supply system and apply in establishing long-term policies for agricultural water resources.

• Journal of Korea Water Resources Association
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• v.46 no.11
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• pp.1115-1128
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• 2013

The purpose of this study is to evaluate the suspended solids and eutrophication processes relationships in Chungju lake using CE-QUAL-W2, two-dimensional (2D) longitudinal/vertical hydrodynamic and water quality model. For water quality modeling, the lake segmentation was configured as 7 branches system according to their shape and tributary distribution. The model was calibrated (2010) and validated (2008) using 2 years of field data of water temperature, suspended solids (SS), total nitrogen (TN), total phosphorus (TP) and algae (Chl-a). The water temperature began to increase in depth from April and the stratification occurred at about 10 m early July heavy rain. The high SS concentration of the interflow density currents entering from the watershed was well simulated especially for July 2008 heavy rainfall event. The simulated concentration range of TN and TP was acceptable, but the errors might occur form the poor reflection for sedimentation velocity of nitrogen component and adsorption-sediment of phosphorus in model. The concentration of Chl-a was simulated well with the algal growth patterns in summer of 2010 and 2008, but the error of under estimation may come from the use of width-averaged velocity and concentration, not considering the actual to one side inclination by wind effect.

• Journal of Korea Water Resources Association
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• v.46 no.6
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• pp.655-666
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• 2013

Lately, intensity and frequency of natural disasters such as flood are increasing because of abnormal climate. Casualties and property damages due to large-scale floods such as Typhoon Rusa in 2002 and Typhoon Maemi in 2003 rapidly increased, and these show the limits of the existing disaster prevention measures and flood forecasting systems regarding irregular climate changes. In order to efficiently respond to extraordinary flood, it is important to provide effective countermeasures through an inundation model that can accurately simulate flood inundation patterns. However, the existing flood inundation analysis model has problems such as excessive take of analysis time and accuracy of the analyzed results. Therefore, this study conducted a flood inundation analysis by using the Gerris flow solver that uses quadtree grid, targeting the Baeksan Levee in the Nakdong River Basin that collapsed because of a concentrated torrential rainfall in August, 2002. Through comparisons with the FLUMEN model that uses unstructured grid among the existing flood inundation models and the actual flooded areas, it determined the applicability and efficiency of the quadtree grid-based flood inundation model of the Gerris flow solver.

• Journal of Wetlands Research
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• v.21 no.4
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• pp.354-364
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• 2019

Sri Lanka is an island nation susceptible to climate-related disasters and extreme weather events. Kurunegala City is the developing capital city of the North-Western Province of Sri Lanka. Changes in rainfall patterns and a steadily increasing annual average temperature amounting to 0.69±0.37℃ were observed in the city area. Generally, urban areas are at risk due to the lack of climate change adaptation provisions incorporated in the development plans. This study was conducted to investigate the characteristics of Krunegala City, Sri Lanka and develop an appropriate climate change adaptation plan for the city. Site investigation and qualitative risk assessment were conducted to devise a plan relevant to the climate change adaptation needs of the city. Qualitative risk analyses revealed that drinking water, water resources, and health and infrastructure risks were among the major concerns in Kurunegala City. Low impact development (LID) technologies were found to be applicable to induce non-point source pollutant reduction, relieve urban heat island phenomenon, and promote sound water circulation systems. These technologies can be effective means of alleviating water shortage and reducing urban temperature. The measures and strategies presented in this study can serve as reference for developing climate change adaptation plans in areas experiencing similar adverse effects of climate change.

• Journal of Korean Society of Forest Science
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• v.103 no.1
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• pp.98-104
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• 2014

This study aims to predict the amount of soil loss from Mt. Palgong's small basin, by using influence factors derived from related models, including RUSLE and MUSLE models, and verify the validity of the model through a comparative analysis of the predicted values and measured values, and the results are as follows: The amount of soil loss were greatly affected by LS factor. In comparison with the measured value of the amount of total soil loss, the predicted values by the two models (RUSLE and MUSLE), appeared to be higher than those of the measured soil loss. Predicted values by RUSLE were closer to values of measured soil loss than those of MUSLE. However, coefficient of variation of MUSLE were lower, but two model's coefficient of variation in similar partial patterns in the prediction of soil loss. RUSLE and MUSLE, prediction soil loss models, proved to be appropriate for use in small mountainous basin. To improve accuracy of prediction of soil loss models, more effort should be directed to collect more data on rainfall-runoff interaction and continuous studies to find more detailed influence factors to be used in soil loss model such as RUSLE and MUSLE.

• Journal of Environmental Impact Assessment
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• v.13 no.4
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• pp.197-211
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• 2004

Visibility impairment was known as an indicator of the increased air pollution. In many previous studies, it is known that both directly emitted fine particles mainly from vehicles and secondary aerosols from photochemical reactions could contribute to this visibility impairment in addition to the meteorological condition. Furthermore, the visibility showed different change patterns according to the geographical condition. In order to research into the influence of these factors on visibility, this study analyzed the visibility at 15:00, observed from 1990 to 2001 in Seoul, Ganghwa, Susan, Gwangju, Jeju. As a result, the visibility was increased in Seoul except the rainfall period, but in Susan, Gwangju, Jeju, it decreased with the relative humidity (RH). Especially, in Seoul, the number of low visibility days was larger than other sites and variations of the visibility were sensitive to the concentration of air pollutants, such as TSP, $NO_2$, $O_3$. The visibility impairment was mainly observed in meteorological condition of RH<50% and relatively stationary front. Therefore it is inferred that photochemical smog could lead to the low visibility in Seoul. On the other hands, in Ganghwa and Susan, when RH was 60~70%, the low visibility observed under the influence of the transports of air pollutants from nearby cities as well as humid air mass from coastal region. And in Jeju, sea fog and humid air mass caused the visibility impairment when the RH was larger than 80%. Finally, during the observational period, some cases of low visibility phenomena were simultaneously observed in the vast region including Seoul, Susan, Ganghwa. It not only includes the visibility aggravation by Asian Dust, but also could be caused by the movement and diffusion of flying dust or secondary aerosols. Moreover, the result shows that these phenomena could be mainly influenced by meteorological factors covering the wide regions.

• Journal of Korean Society of Environmental Engineers
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• v.34 no.9
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• pp.597-603
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• 2012

To investigate the effects of weir construction on sediment characteristics of river bed, we conducted sediments sampling on the 9 locations near the weir, Kangchun, Yuju and Ipo in Namhan-River. Physical and chemical characteristics of sediments were analyzed by measuring particle size distribution, water content, Ignition loss, COD (Chemical Oxyzen Demand), TOC (Total Organic Carbon), TP (Total Phosphorus), SRP (Soluble Reactive Phosphorus) and TN (Total Nitrogen). Particle classification of all three weir sediments showed sandy loam that was caused by the river bed dredging. Due to the presence of weir, Ignition loss, COD, TOC, TP, SRP and TN showed similar trend such as the concentrations of upward weir had higher than those of downward weir. For the case of SRP concentration and C/N ratio, however, there is not much difference in the sediment characteristics compared to the those of sediments before weir construction. Therefore, It can be predicted that there are little effects of weir construction on sediment characteristics. However, weir construction could influence water quality of the river by controlling the transport and the accumulation of suspended materials from rainfall. Therefore, more intensive monitoring is required to examine the magnitude and patterns of sediment accumulation which could influence overlying water quality.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.120-134
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• 2015

InVEST Water Yield model developed by Natural Capital Project was applied for South Korea to assess domestic forest ecosystem's water provisioning services. The InVEST Water Yield model required 8 input dataset, including six spatial map data and two derived by coefficients. By running the model with relatively easy acquired and modified data, the result of domestic forest ecosystem's water provisioning services was 9,409,622,083 ton using the standard of the year 2011. The result showed similar patterns and distribution of rainfall in 2011, but showed difference when compared with existing researches spatially driven in nationwide statistical analysis results. This difference is assumed to occur with different model mechanism in spatial implementation and statistical analysis. So given that the model is currently still developing, applications should be taken on qualitative perspectives rather than on quantitative perspectives. Additionally, for advancing the application of InVEST water yield model, quantification of suitable input data and comparison using multi-modeling is required.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.