In the Geumho River, 4-nitrophenol has been detected, thus it is necessary to investigate the contamination sources in order to prevent the release of this compound. However, the research to estimate the potential source is regarded as complicated research. In this study, the distributions of 4-nitrophenol were simulated and the contribution of the potential sources was estimated using a numerical model(HydroGeoSphere; HGS) and the measuring data of 4-nitrophenol from 2013 to 2017. The altitude data, the land cover data, the flow rates of the tributaries and wastewater treatment plants, and the decay rate of 4-nitrophenol was used as the input data. The results of this research showed that the contribution rates of potential contamination sources in the upstream area were higher than that of the downstream area. Most of the upstream area is the agricultural area, it seemed that 4-nitrophenol was originated from the pesticides. In order to achieve more specific location of sources, an intensive investigation in the upstream is required.

1994년 6월부터 8월까지 금호강 본류의 유수변 초본식생을 식물사회학적으로 조사하였으며, 수질환경과 관련된 식생의 분포특성을 밝혔다. 금호강 본류의 하변식생은 여뀌-미꾸리낚시군락(Persicaria sieboldi-Persicaria hydropiper community), 고마리군락(Persicaria thunbergii community), 물봉선군락(Impatiens textori community), 줄군락(Zizania latifolia co

한국에서 현재 사용되고 있는 홍수예보모형은 집중형 강우-유출모형을 적용하여 유역의 유출을 계산하고 하도 및 저수지 추적모형 등을 활용하여 하천의 수위를 예측한다. 집중형 모형은 유역을 동질의 배수구역으로 가정한다. 따라서 유역내의 다양한 공간적 특성을 고려하지 못한다는 단점이 있다. 또한, 사용되는 강우자료도 지점강우를 활용하기 때문에 공간적인 분포를 자세히 고려하지 못한다는 한계가 있다. 따라서 홍수예보모형에 분포형 모형을 적용하기 위한 연구가 다양하게 진행되고 있다. 본 연구에서는 GRM모형을 한국 홍수예보시스템에 적용하기 위해 모형의 다양한 해상도에 따른 유역유출의 결과의 차이를 분석하여 최적의 해상도를 결정하고자 한다. 모형의 격자가 너무 조밀한 경우 계산시간이 과다하게 되어 홍 수예보모형에 적용하기에는 적합하지 않다. 너무 성길 경우에도 분포형 모형을 적용하여 공간적인 분포를 파악하고자 하는 목적에 맞지 않게 된다. 본 연구의 결과로 유역유출 예측의 정확성을 만족시키고 홍수예보에 적합한 계산속도가 나올 수 있는 최적 해상도를 제시하였다. 유출량 예측의 정 확도는 Nash-Sutcliffe model efficiency coefficient (NSE) 값의 비교를 통해 분석하였다. 본 연구에서 도출된 최적해상도 산정 결과는 분포형 유 역유출모형을 홍수예보모형에 적용하기 위한 기초자료로 활용될 것이다.

In this study, water quality data of eight main sites in the Geumho River watershed were collected and analyzed for long-term changes in water quality over the period from 2005 to 2015. The results showed that BOD concentration was gradually improved by the Total Maximum Daily Load (TMDL), stages 1 and 2. Recently, a tendency of increasing BOD concentration was observed in the downstream section of the river. The concentration of COD was analyzed to be contaminated throughout the water system regardless of the water quality improvement project, and the TN concentration tended to increase in the midstream of the river from 2013. The TP concentration has clearly decreased from 2012 after the second stage of TMDL. For the statistical analysis of PCA ordination, monthly water qualities (pH, DO, Electrical Conductivity (EC), Water Temperature (WT), BOD, COD, TN, TP, TOC, and SS) and flow rate data for 5 years from 2012 to 2016 were used. Seasonally the Geumho River showed an increase in the TN concentration at point sources during the dry season (December to February). TP showed the effect of non-point sources in the summer, because rainfall has caused a rise in flow rate in the upstream. Besides, the origin of pollution source was changed from non-point sources with BOD, COD, and TOC.

For areas with the diverse contamination sources, the change of 4-nitrophenol contamination and impact of potential contamination sources have been evaluated using monitoring data and a numerical model (HydroGeoSphere). The model considered several parameters including land cover, precipitation, and flow rate. And, the model has been performed to investigate the effect of decay rate of 4-nitrophenol. The results of the simulations showed that the influence on 4-nitrophenol in downstream was mainly greater than that in upstream, and the tributaries did not significantly affect the mainstream because of their low flow rates. In addition, the effect of contamination sources was simulated for each section, then the measured data were higher than the corresponding simulated data in most sections of the Geumho river. In particular, the impact of the potential contamination sources in the upstream area was much higher than that in the other area, thus more monitoring data for the upstream area is required.

In this study, we analyzed on-site monitoring data for 15 tributaries in Geumho watersheds for 3 years (2011-2013) in order to sort out priorities on water quality characteristics and improvement. As a result of estimating contribution to contamination of the tributary rivers, Dalseocheon showed the highest load densities, despite the smallest watershed area, with 22.7% BOD5, 30.7% CODMn, 31.3% TOC and 47.6% TP. After conducting PCA (principal component analysis) and FA (factor analysis) to analyze water quality characteristics of the tributary rivers, the first factor was classified as CODMn, TOC, EC, TP and BOD5, the second factor as pH, Chl-a and DO, the third factor as water temperature and TN, and the fourth factor as SS and surface flow. In addition, arithmetical sum of each factor’s scores based on grading criteria revealed that Dalseocheon and Namcheon were classified into Group A for their highest scores - 96 and 93, respectively -, and selected as rivers that require water environmental management measures the most. Also, water environmental contamination inspection showed that Palgeocheon had the most number of aquatic factors to be controlled: BOD5, CODMn, SS, TOC, T-P, Chl-a, etc.

The government has conducted a plan of total maximum daily loads(TMDL), which divides with unit watershed, for management of stable water quality target by setting the permitted total amount of the pollutant. In this study, BOD concentration trends over the last 10 years from 2005 to 2014 were analyzed in the Geumho river. Improvement effect of water quality throughout the implementation period of TMDL was evaluated using the seasonal Mann-Kendall test and a LOWESS(locally weighted scatter plot smoother) smooth. As a study result of the seasonal Mann-Kendall test and the LOWESS smooth, BOD concentration in the Geumho river appeared to have been reduced or held at a constant. As a result of quantitatively analysis for BOD concentration with exploratory data analysis(EDA), the mean and the median of BOD concentration appeared in the order of GH8 〉GH7 〉GH6 〉GH5 〉GH4 〉GH3 〉GH2 〉GH1. The monthly average concentration of BOD appeared in the order of Apr 〉Mar 〉Feb 〉May 〉Jun 〉Jul 〉Jan 〉Aug 〉Sep 〉Dec 〉Nov 〉Oct. As a result of the outlier, its value was the most frequent in February, which is estimated 1.5 times more than July, and was smallest frequent in July. The outlier in terms of water quality management is necessary in order to establish a management plan for the contaminants in watershed.

As a result of analysis based on the observed data for BOD, COD and TOC in order to manage non-biodegradable organics in the Geumho River, COD/BOD ratio was analyzed as the occupying predominance proportion. In this study, the classification(changes in water quality measurement : increase, equal, decrease) and measurement of BOD and COD were analyzed for trends over the past 10 years from 2005 to 2014 in the Geumho River. The Geumho River is expected to need non-biodegradable organics management because BOD was found to be reduced 61.1% and COD was found to be increased 50%. As a result of the analysis of land use, the Geumho-A is a unit watershed area of 921.13 km2, which is the most common area that is occupied by forests. The Geumho-B is a unit watershed area of 436.8 km2, which is the area that is highest occupied by agriculture and grass of 24.84%. The Geumho-C is a unit watershed area of 704.56 km2 accounted for 40.29% of the entire watershed, which is the area that is occupied by urban of 15.12%. Load of non-biodegradable organics, which is not easy biodegradable according to the discharge, appeared to be increased because flow coefficient of COD and TOC at the Geumho-B were estimated larger than 1 value. The management of non-point sources of agricultural land is required because the Geumho-B watershed area occupied by the high proportion of agriculture and field. In this segment it showed to increase the organics that biodegradation is difficult because the ratio of BOD and TOC was decreased rapidly from GR7 to GR8. Thus, countermeasures will be required for this.