재현기간(再現期間)에 따른 단시간(短時間) 강우강도특성(降雨强度特性)을 분석고찰(分析考察)하여 도시하수도(都市下水道) 및 중소유역(中小流域)의 배수계획(排水計劃)과 같은 수리구조물(水理構造物)의 설계(設計)에 필요(必要)한 최적강우강도식(最適降雨强度式)을 대구(大邱)와 포항(浦項)을 대표지점(代表地點)으로 분석(分析)한 결과(結果) 다음과 같은 결론(結論)을 얻었다. 1. 각종(各種) 확률강우강도식(確率降雨强度式) 산정(算定)에 있어서 대구(大邱)는 lwai 법(法), 포항(浦項)은 Gumbel-Chow 법(法)에 의한 결과치(結果値)를 확률강우강도(確率降雨强度)로 채택(採擇)함이 타당(妥當)하다고 생각된다. 2. 최적강우강도식(最適降雨强度式)을 유도(誘導)함에 있어서 표준편차비교결과(標準偏差比較結果) 대구(大邱)는 2.52~4.17, 포항(浦項)은 1.86~4.54로 공(共)히 Japanese 형(型) ()이 적합(適合)한 것으로 나타났으며, 재현기간별(再現期間別) 강우강도식(降雨强度式)은 다음과 같다. 대구(大邱) T : 200년(年) - T : 100년(年) - T : 30년(年) - T : 20년(年) - T : 10년(年) - T : 5년(年) - 포항(浦項) T : 200년(年) - T : 100년(年) - T : 50년(年) - T : 30년(年) - T : 20년(年) - T : 10년(年) - T : 5년(年) - 3. 각(各) 지방(地方)에 따르는 재현기간별(再現期間別) 강우강도(降雨强度)를 쉽게 이용(利用)할 수 있도록 I.D.F. 상관도(相關圖)를 작성(作成)한 바 그 이용도(利用度)의 가치(價値)가 크게 있을 것으로 기대(期待)된다.

전국 9개 고정조사지에서 1968년이래 13개년간 조사한 솔나방의 10월의 밀도를 종합검토한바 다음과 같은 결과를 얻었다. 1. 13개년간에 솔나방의 10월밀도가 특히 높았던 년도는 '70년과 75년의 2회였으며 '76년이후는 단속 저밀도를 유지하고 있다. 2. 밀도가 현저하게 감소하였던 년도는 72년도와 76년도로서 8월중의 강우량 특히 24시간내 최대강우량이 밀접한 관계를 가지고 있었다. 3. 9개 조사지의 솔나방밀도증감은 대체적으로 유사한 경향을 보였으며 이것은 전국적인 강우의 대체적인 동시성에 기인한다고 본다. 4 8월중 일부 최다강우량이 100mm을 넘을 경우 솔나방밀도가 감소하는 확률은 였다.

In this study, the hydraulic gradient was calculated using the groundwater level and rainfall observed in the Hyogyo-ri area for a year, and the change in the hydraulic gradient according to the rainfall was analyzed. It was found that the groundwater level increased as the rainfall increased in all groundwater wells in the research site, and the groundwater level rise decreased as the altitude of the groundwater well increased. The hydraulic gradient in the research site ranged from 0.016 to 0.048, decreasing during rainfall and increasing after the end of the rainfall. As the rainfall increased, the groundwater level rise in the low-altitude area was more than the high-altitude area, and the hydraulic gradient decreased due to the difference in groundwater level rise according to the altitude. Through this study, it was found that the influence of rainfall is dominant for the fluctuation of the hydraulic gradient in the unconfined aquifer.

In this study, not only the increase in rainfall for a short period of time but also the increase in rainfall for a longer duration is frequently occurring according to climate change. Changes in rainfall patterns due to climate change are increasing damage to steep slopes. The Ministry of Public Administration and Security has been operating the criteria for evacuation of residents in steep slopes since 2015. However, the damage to steep slopes due to torrential rains in 2020 has been increasing. In this study, rainfall data from areas affected by steep slopes from 1999 to 2020 were collected and compared with the existing criteria(2015) for evacuation of residents at steep slopes of the Ministry of Public Administration and Security, and the status of the issuance of resident evacuation forecast was compared. Through this study, the rainfall criteria for each region were calculated and presented by reflecting the rainfall characteristics of the steep slope destruction area due to climate change, and it is believed that it can be used as a standard rainfall to reduce human casualties in the steep slope area in the future.

In this study, we investigated the characteristics of the meteorological and environmental conditions for a cloud seeding experiment over the Korean peninsula and estimated the available days for the same. The conditions of available days appropriate for a cloud seeding experiment were classified according to four purposes: water resources, drought relief, forest fire prevention, and air quality improvement. The average number of available days for a cloud seeding experiment were 91.27 (water resources), 45.93–51.11 (drought relief), 40.28–46.00 (forest fire prevention), and 42.19–44.60 days/year (air quality improvement). If six experiments were carried out per available day for a cloud seeding experiment, the number of times cloud seeding experiments could be conducted per year in a continuously operating system were estimated as 547.62 (water resources), 275.58–306.66 (drought relief), 241.68–276.00 (forest fire prevention), and 253.14–267.60 times/year (air quality improvement). From this result, it was possible to determine the appropriate meteorological and environmental conditions and statistically estimate the available days for a cloud seeding experiment. The data on the available days for a cloud seeding experiment might be useful for preparing and performing such an experiment.

In this paper, among the W-S-R relationship methods proposed by Lee, et. al., (2020) to produce rain-based rain information in real time, we tried to produce actual rain information by applying machine learning techniques to take into account the effects of wiper operation. To this end, rain sensor proposed the Graded Descent and Threshold Map method for pre-processing the cumulative value of the difference before and after wiper operation by utilizing four sensitive channels for optical sensors developed by Kim Byung-sik (2016) and using rain sensor data produced by five rain conditions in indoor artificial rainfall experiments. This method is the method of producing rainfall information by calculating the average value of the Threshold according to the rainfall conditions and channels, creating a Threshold Map corresponding to the grid 4 (channel) x 5 (thinking of rainfall information) and applying Optima Rainfall Intensity among the big data processing techniques. For the verification of these proposed findings, the application was evaluated by comparing the rainfall observations with the methods presented by Lee, et. al., (2020).

In this study, the prediction technology of Hydrological Quantitative Precipitation Forecast (HQPF) was improved by optimizing the weather predictors used as input data for machine learning. Results comparison was conducted using bias and Root Mean Square Error (RMSE), which are predictive accuracy verification indicators, based on the heavy rain case on August 21, 2021. By comparing the rainfall simulated using the improved HQPF and the observed accumulated rainfall, it was revealed that all HQPFs (conventional HQPF and improved HQPF 1 and HQPF 2) showed a decrease in rainfall as the lead time increased for the entire grid region. Hence, the difference from the observed rainfall increased. In the accumulated rainfall evaluation due to the reduction of input factors, compared to the existing HQPF, improved HQPF 1 and 2 predicted a larger accumulated rainfall. Furthermore, HQPF 2 used the lowest number of input factors and simulated more accumulated rainfall than that projected by conventional HQPF and HQPF 1. By improving the performance of conventional machine learning despite using lesser variables, the preprocessing period and model execution time can be reduced, thereby contributing to model optimization. As an additional advanced method of HQPF 1 and 2 mentioned above, a simulated analysis of the Local ENsemble prediction System (LENS) ensemble member and low pressure, one of the observed meteorological factors, was analyzed. Based on the results of this study, if we select for the positively performing ensemble members based on the heavy rain characteristics of Korea or apply additional weights differently for each ensemble member, the prediction accuracy is expected to increase.

In this study, we investigated the optimal meteorological conditions for cloud seeding using aircraft over the Korean Peninsula. The weather conditions were analyzed using various data sources such as a weather chart, upper air observation, aircraft observation, and a numerical model for cloud seeding experiments conducted from 2018 to 2019 by the National Institute of Meteorological Sciences, Korea Meteorological Administration. Cloud seeding experiments were performed in the seasons of autumn (37.0%) and winter (40.7%) in the West Sea and Gangwon-do. Silver iodide (70.4%) and calcium chloride (29.6%) were used as cloud seeding materials for the experiments. The cloud seeding experiments used silver iodide in cold clouds. Aircraft observation revealed relatively low temperatures, low liquid water content, and strong wind speeds in clouds with a weak updraft. In warm clouds, the cloud seeding experiments used calcium chloride. Observations included relatively high temperatures, high liquid water content, and weak wind speeds in clouds with a weak updraft. Based upon these results, we determined the comprehensive meteorological conditions for cloud seeding experiments using aircraft over the Korean Peninsula. The understanding of optimal weather conditions for cloud seeding gained from this study provide information critical for performing successful cloud seeding and rain enhancement.

In meteorological data, various studies are being conducted to improve the prediction performance of rainfall with irregular patterns, unlike temperature and solar radiation with certain patterns. Especially in the case of the short-term forecast model for Dong-Nae Forecasts provided by the Korea Meteorological Administration (KMA), forecast data are provided at 6-hour intervals, and there is a limit to analyzing the impact of disasters. In this study, Hydrological Quantitative Precipitation Forecast (HQPF) information was generated by applying the machine learning method to Local ENsemble prediction system (LENS), Radar-AWS Rainrates (RAR), AWS and ASOS observation data and Dong-Nae Forecast provided by the KMA. Through the preprocessing process, the temporal and spatial resolutions of all the data were converted to the same resolution, and the predictor of machine learning was derived through the factor analysis of the predictor. Considering the processing speed and expandability, the XGBoost method of machine learning was applied, and the Probability Matching (PM) method was applied to improve the prediction accuracy of heavy rainfall. As a result of evaluating the HQPF performance produced for 14 heavy rainfall events that occurred in 2020, it was found that the predicted performance of HQPF was improved quantitatively and qualitatively.

Due to recent heavy rain events, there are increasing demands for adapting infrastructure design, including drainage facilities in urban basins. Therefore, a clear definition of urban rainfall must be provided; however, currently, such a definition is unavailable. In this study, urban rainfall is defined as a rainfall event that has the potential to cause water-related disasters such as floods and landslides in urban areas. Moreover, based on design rainfall, these disasters are defined as those that causes excess design flooding due to certain rainfall events. These heavy rain scenarios require that the design of various urban rainfall facilities consider design rainfall in the target years of their life cycle, for disaster prevention. The average frequency of heavy rain in each region, inland and coastal areas, was analyzed through a frequency analysis of the highest annual rainfall in the past year. The potential change in future rainfall intensity changes the service level of the infrastructure related to hand-to-hand construction; therefore, the target year and design rainfall considering the climate change premium were presented. Finally, the change in dimensional safety according to the RCP8.5 climate change scenario was predicted.

The determination of soil parameters is important in predicting the simulated surface runoff using either a distributed or a lumped rainfall-runoff model. Soil characteristics can be collected using remote sensing techniques and represented as a digital map. There is no universal agreement with respect to the determination of a representative parameter from a gridded digital map. Two representative methods, i.e., arithmetic and predominant, are introduced and applied to both FLO-2D and HEC-HMS to improve the model’s accuracy. Both methods are implemented in the Yongdam catchment, and the results show that the former seems to be more accurate than the latter in the test site. This is attributed to the high conductivity of the dominant soil class, which is A type.

The determination of soil characteristics is important in the simulation of rainfall runoff using a distributed FLO-2D model in catchment analysis. Digital maps acquired using remote sensing techniques have been widely used in modern hydrology. However, the determination of a representative parameter with spatial scaling mismatch is difficult. In this investigation, the FLO-2D rainfall-runoff model is utilized in the Yongdam catchment to test sensitivity based on three different methods (mosaic, arithmetic, and predominant) that describe soil surface characteristics in real systems. The results show that the mosaic method is costly, but provides a reasonably realistic description and exhibits superior performance compared to other methods in terms of both the amount and time to peak flow.

기존의 Markov Chain 모형으로 일강우량 모의시에 강우의 발생여부를 모의하고 강우일의 강우량은 Monte Carlo 시뮬레이션을 통해 일강우 분포 특성에 맞는 분포형에서 랜덤으로 강우량을 추정하는 것이 일반적이다. 이때 강우 지속기간에 따른 강도 및 강우의 시간별 분포 등의 강우 사상의 특성을 반영할 수 없다는 한계가 있다. 본 연구에서는 이를 개선하기 위해 강우 사상을 1일 지속강우, 2일 지속강우, 3일 지속강우, 4일이상 지속강우로 구분하여 강우의 지속기간에 따라 강우량을 추정하였다. 즉 강우 사상의 강우 지속일별로 총강우량의 분포형을 비매개변수 추정이 가능한 핵 밀도추정(Kernel Density Estimation, KDE)를 적용하여 각각 추정하였고, 강우가 지속될 경우에 지속일별로 해당하는 분포형에서 강우량을 구하였다. 각 강우사상에 대해 추정된 총 강우량은 k-최근접 이웃 알고리즘(k-Nearest Neighbor algorithm, KNN)을 통해 관측 강우자료에서 가장 유사한 강우량을 가지는 강우사상의 강우량 일분포 형태에 따라 각 일강우량으로 분배하였다. 본 연구는 기존의 강우량 추정 방법의 한계점을 개선하 고자 하였으며, 연구 결과는 미래 강우에 대한 예측에도 활용될 수 있으며 수자원 설계에 있어서 기초자료로 활용될 수 있을 것으로 기대된다.

For the purposes of enhancing usability of Numerical Weather Prediction (NWP), the quantitative precipitation prediction scheme by machine learning has been proposed. In this study, heavy rainfall was corrected for by utilizing rainfall predictors from LENS and Radar from 2017 to 2018, as well as machine learning tools LightGBM and XGBoost. The results were analyzed using Mean Absolute Error (MAE), Normalized Peak Error (NPE), and Peak Timing Error (PTE) for rainfall corrected through machine learning. Machine learning results (i.e. using LightGBM and XGBoost) showed improvements in the overall correction of rainfall and maximum rainfall compared to LENS. For example, the MAE of case 5 was found to be 24.252 using LENS, 11.564 using LightGBM, and 11.693 using XGBoost, showing excellent error improvement in machine learning results. This rainfall correction technique can provide hydrologically meaningful rainfall information such as predictions of flooding. Future research on the interpretation of various hydrologic processes using machine learning is necessary.

This study investigates the performance of four Bayesian methods, Random Walk Metropolis (RWM), Hit-And-Run Metropolis (HARM), Adaptive Mixture Metropolis (AMM), and Population Monte Carlo (PMC), for estimating the parameters and uncertainties of probability rainfall distribution, and the results are compared with those of conventional parameter estimation methods; namely, the Method Of Moment (MOM), Maximum Likelihood Method (MLM), and Probability Weighted Method (PWM). As a result, Bayesian methods yield similar or slightly better results in parameter estimations compared with conventional methods. In particular, PMC can reduce parameter uncertainty greatly compared with RWM, HARM, and AMM methods although the Bayesian methods produce similar results in parameter estimations. Overall, the Bayesian methods produce better accuracy for scale parameters compared with the conventional methods and this characteristic improves the accuracy of probability rainfall. Therefore, Bayesian methods can be effective tools for estimating the parameters and uncertainties of probability rainfall distribution in hydrological practices, flood risk assessment, and decision-making support.

This study aimed to assess the impact of livestock excreta discharged from an Intensive Livestock Farming Area (ILFA) on river water quality during a rainfall event. The Bangcho River, which is one of the 7 tributaries in the Cheongmi River watershed, was the study site. The Cheongmi River watershed is the second largest area for livestock excreta discharge in Korea. Our results clearly showed that, during the rainfall event, the water quality of the Bangcho River was severely deteriorated due to the COD, NH4-N, T-N, PO4-P, T-P, and heavy metals (Cu, Zn, and Mn) in the run-off from nearby farmlands, where the soil comprised composted manure and unmanaged livestock excreta. In addition, stable isotope analysis revealed that most of nitrogen (NH4-N and NO3-N) in the run-off was from the ammonium and nitrate in the livestock excreta. The values of δ15NNH4 and δ15NNO3 for the Bangcho River water sample, which was obtained from the downstream of mixing zone for run-off water, were lower than those for the run-off water. This indicates that there were other nitrogen sources upstream river in the river. It was assumed from δ15NNH4 and δ15NNO3 stable isotope analyses that these other nitrogen sources were naturally occurring soil nitrogen, nitrogen from chemical fertilizers, sewage, and livestock excreta. Therefore, the use of physicochemical characteristics and nitrogen stable isotopes in the water quality impact assessment enabled more effective analysis of nitrogen pollution from an ILFA during rainfall events.

본 연구에서는 비슬산 이중편파 Radar 자료와, GPM 위성자료 및 21개 (Korea Meteorological Administration, KMA) 지상강우자료를 활용하여 분포형 강우-유출 모형(KIneMatic wave STOrm Runoff Model2, KIMSTORM2)을 이용해 남강댐 유역(2,293 km2)을 대상으로 유출해석을 수행 하였다. 모형의 유출 해석은 2016년 10월 5일 02:00∼09:00 총 8시간 동안 최대강우강도 33 mm/hr, 유역평균 총 강우량 82 mm이 발생한 태풍 차 바(CHABA)를 대상으로 하였으며, Radar 및 GPM 자료와 조건부합성(Conditional Merging, CM) 기법을 적용한 Radar (CM-corrected Radar) 및 GPM (CM-corrected GPM) 자료를 각각 활용하여 결과를 비교하였다. 이 때, 공간 강우자료에 유출 검보정은 남강댐 유역 내 3개의 수위관측 지점(산청, 창촌, 남강댐)을 대상으로 실시하였으며, 모형의 매개변수 초기토양수분함량, 지표와 하천의 Manning 조도계수를 이용하여 검보정하였다. 유출 결과는 결정계수(Determination coefficient, R2), Nash-Sutcliffe의 모형효율계수(NSE) 및 유출용적지수(Volume Conservation Index, VCI)를 산정하였다. 그 결과 CM-corrected Radar, GPM 자료가 평균 R2는 0.96, NSE의 경우 0.96, 유출용적지수(VCI)는 1.03으로 가장 우수한 결과를 나타내었다. 최종적으로 CM 기법을 이용한 보정된 공간분포자료는 기존의 자료에 비해 시공간적으로 정확한 홍수 예측에 사용 될 것으로 판단된다.

In this study, we identified heavy rain damage and rainfall characteristics for each region, and proposed Hazard-Triggering rainfall according to heavy rain damage scale focused on Gyeonggi-do. We classified the damage scale into three groups (total damage, over 100 million won, over 1 billion won) to identify the characteristics of heavy rain damage, and we determined criteria of the rainfall class for each rainfall variable (maximum rainfalls for the durations of 1, 3, 6, 12 hours) to identify the rainfall characteristics. We calculated the cumulative probability of heavy rain damage based on the rain criteria mentioned above to establish the Hazard-Triggering rainfall according to the heavy rainfall damage scale. Using the results, we establish the Hazard-Triggering rainfall for each rain variable according to heavy rain damage. Finally, this study calculated the assessment indicator (F1-Score) for classification performance to test the performance of the Hazard-Triggering rainfall. As the results, the classification performance of the Hazard-Triggering rainfall which proposed in this study was 11%, 30%, 10% higher than the criteria by KMA (Korea Meteorological Administration).

본 연구의 목적은 동와 조휘진(1729-1797)의 생애와 활동을 밝히는 일이다. 일반적으로 正祖가 남명 조식에게 사제(賜祭)하였던 시기를 남명학 부흥의 시발점으로 일컫는데, 그는 이때 남명학파의 중심인 덕천서원을 대표하는 인물이었다. 따라서 각종 진주 관련 지리지에 그의 이름이 기재되어 있고, 출중한 인물로 많은 인물과 교유하고 있었음을 밝히고 있으나 자세한 연구가 이루어진 바가 아직 없다. 그의 집안은 대대로 함안에 세거하였다. 조선 건국 후 출사를 거부하고 함안에 은거하였던 趙悅, 단종 때의 생육신 가운데 한 사람이었던 趙旅, 임진왜란 때 순사한 趙宗道가 그의 선조이다. 그는 평소 효를 실천함과 동시에 선조의 忠을 드러낸 것 역시 효의 실천으로 보았다. 선조 열 명의 업적을 기록한 『趙氏十忠實錄』은 그 결과물이라 할 수 있다. 아울러 남명학의 전수자로서 덕천서원을 중심으로 유림을 규합하는 역할을 하였다. 그의 문집인 『東窩遺集』에 보이는 창화시 및 만사를 통해 그의 교유범위가 퇴계학파와 중앙의 남인까지 포괄하고 있음을 확인할 수 있다. 기개와 절조를 숭상하고 處士的인 학풍을 견지하던 남명학파의 유풍을 그대로 견지하였던 인물이라 평가할 수 있다.

본 연구에서는 주 단위 지하수자원 관리 취약시기 평가 방법을 개발하였다. 강수의 지하수위에 대한 영향을 고려하기 위하여 한계 침투량을 고려한 강우이동평균 방법을 통해 지하수위와의 상관계수를 산정하였다. 취약 시기 평가 기준을 개발하고 평가 기준에 대한 가중치를 엔트로피 방법을 이용하여 산정하였다. 강수와의 상관계수와 산정된 가중치를 이용한 주 단위 지하수자원 관리 취약시기 평가 방법을 개발하였으며, 개발한 방법을 통하여 소규모 행정구역을 대상으로 취약시기를 평가하였다. 본 연구에서 개발된 방법은 지역적일뿐만 아니라 계절적인 지하수자원의 효율적 관리 대책 수립의 근거가 될 수 있을 것이다.