• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.237-242
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• 2017

Along with effluence of non-point pollution source, continuous precipitation due to rainy season or localized heavy rain can also be a good reason for increase of flow rate. And if the water level is going up due to the increase, floating debris around rivers and streams will move because of increased flow velocity. However, currently, there are no studies which perform quantitative calculation on movement of floating debris by analyzing amount of rainfall and flow rate in both domestic and abroad. Thus, the present study calculated amount of movement of floating debris based on moving route monitoring results according to changes of effective rainfall and flow rate that are obtained by using SCS-CN method.

• Atmosphere
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• v.33 no.5
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• pp.457-475
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• 2023

Accurately predicting localized heavy rainfall is challenging without high-resolution mesoscale cloud information in the numerical model's initial field, as precipitation intensity and amount vary significantly across regions. In the Korean Peninsula, the radar observation network covers the entire country, providing high-resolution data on hydrometeors which is suitable for data assimilation (DA). During the pre-processing stage, radar reflectivity is classified into hydrometeors (e.g., rain, snow, graupel) using the background temperature field. The mixing ratio of each hydrometeor is converted and inputted into a numerical model. Moreover, assimilating saturated water vapor mixing ratio and decomposing radar radial velocity into a three-dimensional wind vector improves the atmospheric dynamic field. This study presents radar DA experiments using a numerical prediction model to enhance the wind, water vapor, and hydrometeor mixing ratio information. The impact of radar DA on precipitation prediction is analyzed separately for each radar component. Assimilating radial velocity improves the dynamic field, while assimilating hydrometeor mixing ratio reduces the spin-up period in cloud microphysical processes, simulating initial precipitation growth. Assimilating water vapor mixing ratio further captures a moist atmospheric environment, maintaining continuous growth of hydrometeors, resulting in concentrated heavy rainfall. Overall, the radar DA experiment showed a 32.78% improvement in precipitation forecast accuracy compared to experiments without DA across four cases. Further research in related fields is necessary to improve predictions of mesoscale heavy rainfall in South Korea, mitigating its impact on human life and property.

• Journal of Korea Water Resources Association
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• v.53 no.7
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• pp.493-505
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• 2020

The rainfall-runoff model has been generally adopted to obtain a consistent runoff sequence with the use of the long-term ground-gauged based precipitation data. The Thiessen polygon is a commonly applied approach for estimating the mean areal rainfall from the ground-gauged precipitation by assigning weight based on the relative areas delineated by a polygon. However, spatial bias is likely to increase due to a sparse network of the rain gauge. This study aims to generate continuous runoff sequences with the mean areal rainfall obtained from radar rainfall estimates through a PRMS rainfall-runoff model. Here, the systematic error of radar rainfall is corrected by applying the G/R Ratio. The results showed that the estimated runoff using the corrected radar rainfall estimates are largely similar and comparable to that of the Thiessen. More importantly, one can expect that the mean areal rainfall obtained from the radar rainfall estimates are more desirable than that of the ground in terms of representing rainfall patterns in space, which in turn leads to significant improvement in the estimation of runoff.

• Journal of Wetlands Research
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• v.7 no.4
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• pp.97-107
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• 2005

As the distributed model is developed and widely used, the accuracy of a rainfall measurement and more dense rainfall observation network are required for the reflection of various spatial properties. However, in reality, it is not easy to get the accurate data from dense network. Generally, we could not have the proper rainfall gages in space and even we have proper network for rainfall gages it is not easy to reflect the variations of rainfall in space and time. Often, we do also have missing rainfall data at the rainfall gage stations due to various reasons. We estimate the distribution of mean areal rainfall data from the point rainfalls. So, in the aspect of continuous rainfall property in time, we should fill the missing rainfall data then we can represent the spatial distribution of rainfall data. This study uses the Fuzzy-Genetic algorithm as a interpolation method for filling the missing rainfall data. We compare the Fuzzy-Genetic algorithm with arithmetic average method, inverse distance method, normal ratio method, and ratio of distance and elevation method which are widely used previously. As the results, the previous methods showed the accuracy of 70 to 80 % but the Fuzzy-Genetic algorithm showed that of 90 %. Especially, from the sensitivity analysis, we suggest the values of power in the equation for filling the missing data according to the distance and elevation.

• Journal of Forest and Environmental Science
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• v.32 no.4
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• pp.353-366
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• 2016

The effect of different environmental conditions and altitudes on the growth and reproductive characteristics in 12 teak plantations at 4 different blocks (Cauvery canal bank, Topslip and Parambikulam (Tamil Nadu), Nilambur and Wayanad (Kerala) of Southern India was investigated. The annual rainfall and mean monthly temperature of the study areas varied significantly from 1390 to 3188 mm and 16 to $38^{\circ}C$ respectively. The teak plantations in Cauvery canal bank which grow in continuous moisture condition (8-10 months) retain the leaf for longer period due to moisture resulting continuous supply of photosynthates leads to fast and outstanding growth. The girth at breast height (GBH) of 34-years-old tree in canal area was similar to that of 40 to 49-years-old trees in other locations, indicating that teak plantations with regular watering and silvicultural practices may be harvested at the age of 30 years. The leaf fall, flowering and fruiting showed significant variations in different teak plantations due to environmental factors and altitudes. It was found that increase of rainfall enhances number of flowers in the inflorescence in teak. Tholpatty (block-IV) showed more flowering in a inflorescence (3,734-3,744) compared to other plantations (1,678-3,307). Flowering in Nilambur and Wayanad coincided with heavy rainfall resulting low fruitset (1.1-2.3%) probably heavy rainfall ensuing restriction of pollinators for effective pollination. On the other hand, flowering in Cauvery canal bank (Block-I) was not coincided with high rainfall exhibited high fruitset (2-3%). About 66 to 76% of the fruits in different plantations were empty, and it is one of the main reasons for poor germination in teak. The seeds of Topslip and Parambikulam (Block-II) showed higher seed weight, maximum seed filling and good germination indicating that the environmental factors and altitude play significant role in fruit setting and seed filling in teak. In addition, the teak plantations in Topslip and Parambikulam showed good growth suggesting that plantations in the altitude range of approximately 550-700 m may be suitable for converting into seed production areas for production of quality seeds.

• Journal of Korea Water Resources Association
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• v.30 no.5
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• pp.441-447
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• 1997

Rainfall observation using rain gage network or satellites includes the sampling error depending on the observation methods or plans. For example, the sampling using rain gages is continuous in time but discontinuous in space, which is nothing but the source of the sampling error. The sampling using satellites is the reverse case that continuous in space and discontinuous in time. The sampling error may be quantified by use of the temporal-spatial characteristics of rainfall and the sampling design. One of recent works on this problem was done by North and Nakamoto (1989), who derived a formulation for estimating the sampling error based on the temporal-spatial rainfall spectrum and the design scheme. The formula enables us to design an optimal rain gage network or a satellite operation plan providing the statistical characteristics of rainfall. In this paper the formula is reviewed and applied for the sampling error problems using several multi-dimensional precipitation models. The results show the limitation of the formulation, which cannot distinguish the model difference in case the model parameters can reproduce similar second order statistics of rainfall. The limitation can be improved by developing a new way to consider the higher order statistics, and eventually the probability density function (PDF) of rainfall.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.3
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• pp.109-121
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• 2020

Converting the agricultural land-use of rice field to upland has been increasingly conducted as farmers encourages themselves to grow higher value-added crops on rice fields under the policy support. Comparing to rice field, Upland shows different characteristic of discharge due to the slope, scale, and shape of field and characteristics of rainfall event. In this study, we designed the experiment fields reflecting flat-upland characteristics with different land scale, and tried to collect the discharge and load data. Soybeans and corn were selected as target crops considering the possibility of large-scale cultivation and crop demand. The cultivation was conducted during the growth period in 2019 with 3 different field scales. Hence, we have collected the discharge data from 17 rainfall events and the load data for 8 rainfall events. As a result, the magnitude of rainfall events and the discharge duration were found to have a strong positive correlation and field discharge occurred during the period by 55% to 83% of rainfall duration. Besides we found other relationships and characteristics of rainfall event, discharge, and pollutant load and also pointed out that continuous monitoring and more data are required to derive statistically significant results. Compared with slope-field monitoring data obtained from the precedent research, the runoff ratio of the flat-fields was significantly lower than slope-fields. Overall the discharge in the slop and flat-fields shows appreciably different characteristics so that the related researches need to be further conducted to reasonably assess environmental impact of agricultural activities at flat-field.

• Journal of Wetlands Research
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• v.15 no.1
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• pp.79-90
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• 2013

Stormwater wetland targeted to treat the rainfall runoff from cow feeding-lot basin has been monitored from May 2010 to November 2011. Reduction efficiency estimated based on 20 rainfall event monitoring was 88%, 54%, 70%, 31%, and 64% for TSS, BOD, $COD_{Cr}$, TN, and TP, respectively. Theoretically, as rainfall depth increases, hydraulic exchange ratio has to be increased. When the exchange ratio approaches to 1 (usually design goal), TSS reduction efficiency was estimated about 55%. Uncertainty in reduction efficiency of the stormwater wetland is normally very high due to the continuous rainfall activity, its magnitude and intensity, antecedent dry days, and other natural variables which can not be controlled by experiment conductors. In this study, predominant affecting variables was found to be hydraulics caused by consecutive rainfall events having different intensity and algal growth during dry days.

• Journal of Korean Society on Water Environment
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• v.24 no.3
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• pp.345-353
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• 2008

In order to design storage-based non-point source management facilities, the aspect of statistical features of the entire precipitation time series should be considered since non-point source pollutions are delivered by continuous rainfall runoffs. The 3-parameter mixed exponential probability density function instead of traditional single-parameter exponential probability density function is applied to represent the probabilistic features of long-term precipitation time series and model urban stormwater runoff. Finally, probability density functions of water quality control basin overflow are derived under two extreme intial conditions. The 31-year continuous precipitation time series recorded in Busan are analyzed to show that the 3-parameter mixed exponential probability density function gives better resolution.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.471-484
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• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.