• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.6
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• pp.137-149
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• 2020

This study was carried out to analyze the monthly runoff concentration on non rainfall days in order to prepare basic data to compare the runoff concentration on rainfall days in 7 forest watersheds in the Republic of Korea. Forest stream water has been collected through 15 times of sampling in each watershed and analyzed based on the changes in concentration of Biochemical Oxygen Demand(BOD), Chemical Oxygen Demand(COD), Total Organic Carbon(TOC), Total Nitrogen(TN), and Total Phosphorus(TP). The average concentration was 0.8 mg/L for BOD, 1.4 mg/L for COD, 0.8 mg/L for TOC, 1.85 mg/L for TN and 0.002 mg/L for TP during non rainfall days. Coniferous forested watersheds showed higher value of TN and TP concentration. Concentrations of BOD and TP in early March (p<0.01) were affected by melt water flow input in spring season. Significant differences (p<0.01) in concentrations were observed in BOD and TOC, indicating seasonal rainfall and vegetation growth impacts on forest stream quality. Concentration of TN and TP showed significant positive correlation, and weak negative correlation was found in the concentration of BOD and TOC. It is expected that result of forest stream water on non rainfall days could be basic information in managing non-point source from forest watersheds.

• Water for future
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• v.28 no.5
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• pp.219-233
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• 1995

A non-stationary multivariate model is selected in which the mean and variance of rainfall are not temporally or spatially constant. And the rainfall prediction system is constructed which uses the recursive estimation algorithm, Kalman filter, to estimate system states and parameters of rainfall model simulataneously. The on-line, real-time, multivariate short-term, rainfall prediction for multi-stations and lead-times is carried out through the estimation of non-stationary mean and variance by the storm counter method, the normalized residual covariance and rainfall speed. The results of rainfall prediction system model agree with those generated by non-stationary multivariate model. The longer the lead time is, the larger the root mean square error becomes and the further the model efficiency decreases form 1. Thus, the accuracy of the rainfall prediction decreases as the lead time gets longer. Also it shows that the mean obtained by storm counter method constitutes the most significant part of the rainfall structure.

• Journal of Korean Society on Water Environment
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• v.34 no.5
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• pp.487-493
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• 2018

Urban stormwater runoff is the one of the most extensive causes of deterioration of water quality in streams in urban areas. Especially, in the Suyeong River watershed, non-point sources from urban-residential areas are the most common cause of water pollution. Also, it has been ascertained that BOD and COD as indexes of organic matter, have limitation on management of Suyeong River's water quality. In this study, changes of organic matter properties of Suyeong River from inflow of non-point source during rainfall were investigated. Fractions of organic matters were analyzed using water samples collected at two sites (Suyeong River and Oncheon Stream) during a rain event. Variations of dissolved organic carbon (DOC) concentration by rainfall were similar to flow rate change in the river. Distribution of organic matter fraction according to change of rain duration revealed that while hydrophilic component increased at initial rainfall, the hydrophobic component was similar to change in dissolved organic carbon (DOC) concentration. Also, the relative proportion of hydrophilic components in organic matter in river water increased, due to rainfall. Results of biodegradation of organic matters revealed that decomposition rate of organic matters during rainfall was higher than that of during a non-rainfall event.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1798-1802
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• 2007

The practice of business estimate flood discharge by rainfall-flow relation that is easy collection of observation data. The important factor is rainfall, coefficient of runoff, and drainage area for analysis of runoff-flow relation.The practice of business usually use probability rainfall that use a weighted average value after each observation post estimate probability of non-same time. It has more error than same time probability rainfall, and it can excess of estimation because it can't consider space distribution of rainfall.The study of result showed similar aspect with existing ARF but width of coefficient become smaller. And the comparison of peak flow did not different what used by ARF and same time probability rainfall(A group). But non-same time probability rainfall is bigger 25% more than another(B group). Between A group and B group of the difference increased with the lapse of time.

• Journal of Industrial Technology
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• v.27 no.A
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• pp.95-102
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• 2007

The practice of business estimate flood discharge by rainfall-flow relation that is easy collection of observation data. The important factor is rainfall, coefficient of runoff, and drainage area for analysis of runoff-flow relation. The practice of business usually use probability rainfall that use a weighted average value after each observation post estimate probability of non-same time. It has more error than same time probability rainfall, and it can excess of estimation because it can't consider space distribution of rainfall. The study of result showed similar aspect with existing ARF but width of coefficient become smaller. And the comparison of peak flow did not different what used by ARF and same time probability rainfall(A group). But non-same time probability rainfall is bigger 25% more than another(B group). Between A group and B group of the difference increased with the lapse of time.

• Journal of Environmental Health Sciences
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• v.31 no.1
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• pp.1-6
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• 2005

This study was conducted to identify the runoff characteristics of non-point source according to rainfall in Nam watershed. Land-uses of the Nam watershed were surveyed paddy field 4.5%, crop field 6.8%, mountainous 78.7%, urban 2.4%, and etc. 7.7%. Mean runoff coefficients in each area were observed Ⅰ area 0.08, Ⅱ area 0.08, and Ⅲ area 0.05. In the relationship between the rainfall and peak-flow, correlation coefficients(r) were investigated Ⅰ area -0.8609, Ⅱ area 0.6035, and Ⅲ area -0.4913. In the relationship between the antecedent dry period and first flow runoff, correlation coefficients(r) were investigated Ⅰ area -0.9093, Ⅱ area -0.1039, and Ⅲ area -0.7317. The discharge of pollutant concentrations relates to the flow rate of storm-water. In the relationship between the rainfall and watershed loading, exponent values of BOD, COD, SS, and T-N were estimated to 1.2751, 1.2003, 1.3744, and 1.1262, respectively.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.8
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• pp.572-577
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• 2011

The unit load has simply been used to estimate total pollutant loading from non-point sources, however, it does not count on the variable pollutant loading according to land use characteristics and rainfall intensity. Since pollutant emission from the watershed is strongly dependent on the rainfall intensity, it is necessary to find out the relationship between pollutant loading and rainfall intensity. The objective of this study is to develop simple and easy method to compute non-point source pollution loads with consideration of rainfall intensity. Two non-point source removal facility at Gyeongan-dong (Gwangju-si) and Mohyeon-myeon (Yongin-si), Gyeonggi-do was selected to monitor total rainfall, rainfall intensity, runoff characteristics and water quality from June to November, 2010. Most of Event Mean Concentrations (EMC) of measured water quality data were higher in Gyeongan which has urban land use than in Mohyeon which has rural land use. For the case of TP (Total Phosphorus), Mohyeon has higher values by the influence of larger chemical uses such as fertilizer. The relationship between non-point source pollution load and rainfall intensity is perfectly well explained by cubic regression with 0.33~0.81 coefficients of determination($R^2$). It is expected that the pollution loading function based on the long-term monitoring would be very useful with good accuracy in computing non-point source pollution load, where a rainfall intensity is highly variable.

• Journal of Korea Water Resources Association
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• v.43 no.2
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• pp.187-199
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• 2010

This study evaluated applicability and confidence of probability rainfalls estimated by the non-stationary rainfall frequency analysis which was recently developed. Using rainfall data at 4 sites which have an obvious increasing trend in observations, we estimated 3 type probability rainfalls; probability rainfalls from stationary rainfall frequency analysis using data from 1973-1997, probability rainfalls from stationary rainfall frequency analysis using data from 1973-2006, probability rainfalls from non-stationary rainfall frequency analysis assuming that the current year is 1997 and the target year is 2006. Based on the comparison of residuals from 3 probability rainfalls, the non-stationary rainfall frequency analysis provided more effective and well-directed estimates of probability rainfalls in the target year. Using Bootstrap resampling, this study also evaluated the parameter estimation methods for the non-stationary rainfall frequency analysis based on confidence intervals. The confidence interval length estimated by the maximum likelihood estimation (MLE) is narrower than the probability weighted moments (PWM). The results indicated that MLE provides more proper confidence than PWM for non-stationary probability rainfalls.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.2
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• pp.265-274
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• 2011

Nowadays, the importance of non-point source pollution treatment is being emphasized. Especially, the easy runoff characteristic of highly concentrated pollutants in the roads makes the circumstance more complicated due to impermeability of roads. When the pollutants flow into steam it could make water quality in stream worse and it also causes a bad influence in the aquatic ecosystem because the effluents of rainfall-runoff may contain indecomposable materials like oil and heavy metals. Therefore, we tried to figure out the property of non-point source pollution when it is raining and carried out an assessment for the property of runoff for non-point source pollution and EMC (Event Mean Concentrations) of the essential pollutants during this study. As the result of the study, the EMC was BOD 5.2~21.7 mg/L, COD 7.5~35.4 mg/L, TSS 71.5~466.1 mg/L, T-N 0.682~1.789 mg/L and T-P 0.174~0.378 mg/L, respectively. The decreasing rate of non-point pollutant in Chungyang-Hongsung road indicates the maximum decrease of 80% until 5 mm of rainfall based on SS concentration; by the rainy time within 20~30 minutes, the decreasing rate of SS concentration was shown as 88.0~97.6%. Therefore it was concluded that it seems to be possibly control non-point pollutants if we install equipments to treat non-point pollutants with holding capacity of 30 min. It is supposed that the result of this study could be used for non-point pollutants treatment of roads in Chungyang-Hongsung area. We also want to systematically study and consistently prepare the efficient management of runoff from non-point source pollution and pollutant loading because the characteristics of non-point source pollution runoff changes depending on different characteristics and situations of roads and rainfall.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.1
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• pp.51-58
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• 2016

On account of the increase in water demand and climate change, droughts are in great concern for water resources planning and management. In this study, rainfall characteristics with stationary and non-stationary perspectives were analyzed using Weibull distribution model with 40-year records of annual minimum rainfall depth collected in major cities of Korea. As a result, the non-stationary minimum probable rainfall was expected to decrease, compared with the stationary probable rainfall. The reliability of ${\xi}_1$, a variable reflecting the decrease of the minimum rainfall depth due to climate change, in Wonju, Daegu, and Busan was over 90%, indicating the probability that the minimal rainfall depths in those city decrease is high.