• 대기
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• 제22권4호
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• pp.387-400
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• 2012

This study aims at examining the sensitivity of numerical simulations to the resolution of initial and boundary data, and to an application of WRF (Weather Research and Forecasting) 3DVAR (Three Dimension Variational data Assimilation). To do this, we ran the WRF model by using GDAS (Global Data Assimilation System) FNL (Final analyses) and the KLAPS (Korea Local Analysis and Prediction System) analyses as the WRF's initial and boundary data, and by using an initial field made by assimilating the radar data to the KLAPS analyses. For the sensitivity experiment, we selected a heavy rainfall case of 21 September 2010, where there was localized torrential rain, which was recorded as 259.5 mm precipitation in a day at Seoul. The result of the simulation using the FNL as initial and boundary data (FNL exp) showed that the localized heavy rainfall area was not accurately simulated and that the simulated amount of precipitation was about 4% of the observed accumulated precipitation. That of the simulation using KLAPS analyses as initial and boundary data (KLAPC exp) showed that the localized heavy rainfall area was simulated on the northern area of Seoul-Gyeonggi area, which renders rather difference in location, and that the simulated amount was underestimated as about 6.4% of the precipitation. Finally, that of the simulation using an initial field made by assimilating the radar data to the KLAPS using 3DVAR system (KLAP3D exp) showed that the localized heavy rainfall area was located properly on Seoul-Gyeonggi area, but still the amount itself was underestimated as about 29% of the precipitation. Even though KLAP3D exp still showed an underestimation in the precipitation, it showed the best result among them. Even if it is difficult to generalize the effect of data assimilation by one case, this study showed that the radar data assimilation can somewhat improve the accuracy of the simulated precipitation.

• 한국지구과학회지
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• 제39권6호
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• pp.533-544
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• 2018

강수는 기상학, 농업, 수문학, 자연재해, 토목 및 건설 등 분야에서 매우 중요한 기상 변수들 중 하나이다. 최근 이러한 강수를 탐지하고, 측정 및 예보를 하기 위해서 위성원격탐사기술은 필수적이다. 따라서 본 연구에서는 미국항공우주국(National Aeronautics and Space Administration, NASA)에서 발사한 전 지구 강수 관측 위성인 GPM 위성을 기반으로 다양한 자료와 합성된 강수 자료인 IMERG 자료의 정확도를 한반도, 특히 남한지역에 대해 지상관측자료와 비교분석 하였다. 기상자동관측 장비인 AWS의 관측 강수량을 검증 자료로 사용하여, 2016년 1월부터 12월까지 1년간의 기간 동안 한반도의 육상부분에 대하여 IMERG의 월 강수량 자료를 비교 검증하였다. 잘 알려진 대로 위성은 해안가와 섬 지역 같은 부분에서 단점이 있지만, 별도로 비교 분석하였다. 위성 자료인 IMERG와 지상 관측 자료인 AWS를 비교한 결과, 상관계수가 0.95로 높은 상관성을 보였으며, Bias, RMSE의 오차 비교에서도 각각 월 15.08 mm, 월 30.32 mm의 낮은 오차를 산출하였다. 해안지역에서도 육상지역과 마찬가지로 0.7 이상의 높은 상관계수를 산출하며, 강수 자료로서 IMERG의 신뢰도를 검증하였다.

• 대한토목학회논문집
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• 제26권1B호
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• pp.61-67
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• 2006

강한 비선형성의 경향을 보이고 있는 강우-유출간의 관계를 모형화하기 위한 연구는 다양한 방법론으로 적용되어 활발히 연구되고 있다. 그 중에서 인공신경망을 이용하여 강우-유출간의 관계를 모형화하기 위한 대부분의 연구들은 역전파 학습 알고리즘(back propagation algorithm: BPA), Levenberg Marquardt(LV), radial basis function(RBF)을 이용하였으며, 이들은 강한 비선형성을 나타내는 입 출력간의 관계를 나타내는데 탁월한 성능을 보이고 있는 것으로 알려져 있고, 자료들의 급격한 변화나 현저한 변화에 대한 뛰어난 적응성을 보여주고 있다. 이러한 인공신경망 이론은 예측뿐만이 아니라 대상자료들의 양상을 분류하여 그 특성을 분석하는 데에도 이용되고 있다. 따라서 본 연구에서는 강우-유출과정의 양상에 따른 분류와 그에 따른 분석을 위해 Kohonen 네트워크 이론에 의한 자기조직화 방법(self-organizing map; SOM)을 적용하였다. 본 연구에서 제시한 방법을 이용한 결과, 강우의 시 공간적 분포의 불규칙한 변동성을 고려한 강우양상을 분류 할 수 있었으며, 강우-유출간의 특성을 분석한 결과 강한 비선현성을 가지고 있는 강우-유출관계가 SOM에 의해 7개의 패턴으로 구분되었다.

• 한국농공학회논문집
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• 제52권1호
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• pp.13-18
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• 2010

In this study the validities of runoff prediction methods are reviewed around ESP (Ensemble Streamflow Prediction) techniques. The improvements of runoff predictions on Yongdam river basin are evaluated by the comparison of different prediction methods including ESP incorporated with qualitative meteorological outlooks provided by meteorological agency as well as the runoff forecasting based on the analysis of the historical rainfall scenarios. As a result it is assessed that runoff predictions with ESP may give rise to more accurate results than the ordinary historical average runoffs. In deed the latter gave the mean of yearly absolute error as to be 60.86 MCM while the errors of the former ones amounted to 44.12 MCM (ESP) and 42.83 MCM (ESP incorporated with qualitative meteorological outlooks) respectively. In addition it is confirmed that ESP incorporated with qualitative meteorological outlooks could improve the accuracy of the results more and more. Especially the degree of improvement of ESP with meteorological outlooks shows rising by 10.8% in flood season and 8% in drought season. Therefore the methods of runoff predictions with ESP can be further used as the basic forecasting information tool for the purpose of the effective watershed management.

• Environmental Engineering Research
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• 제24권3호
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• pp.404-411
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• 2019

In this study, we designed a data-driven model to predict chlorophyll-a using M5P model tree and extreme learning machine (ELM). The Juksan weir in the Youngsan River has high chlorophyll-a, which is the primary indicator of algal bloom every year. Short-term algal bloom prediction is important for environmental management and ecological assessment. Two models were developed and evaluated for short-term algal bloom prediction. M5P is a classification and regression-analysis-based method, and ELM is a feed-forward neural network with fast learning using the least square estimate for regression. The dataset used in this study includes water temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a, which were collected on a daily basis from January 2013 to December 2016. The M5P model showed that the prediction model after one day had the highest performance power and dropped off rapidly starting with predictions after three days. Comparing the performance power of the ELM model with the M5P model, it was found that the performance power of the 1-7 d chlorophyll-a prediction model was higher. Moreover, in a period of rapidly increasing algal blooms, the ELM model showed higher accuracy than the M5P model.

• 한국물환경학회지
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• 제28권1호
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• pp.1-9
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• 2012

It has been known that Golf course could impose negative impacts on water-ecosystem if pollutant-laden runoff is not treated well. It is important to control non-point source and re-use treated wastewater from the golf course to secure water quality of receiving waterbodies. At golf courses, the rainfall-runoff is affected by various practices to manage grasses. In many hydrological modelings, especially in simple rainfall-runoff modeling, effects on runoff of plant growth and cutting are not considered. In the study, the water erosion prediction project (WEPP), capable of simulating plant growth and various management, was evaluated for its runoff prediction from golf course under grass cutting and irrigation. The %Difference, $R^2$ and the NSE for runoff comparisons were 1.15%, 0.93 and 0.92 for calibration, and 18.12%, 0.82 and 0.88 for validation period, respectively. In grass cutting scenario, grass height was managed to be 18~25 mm. The estimated runoff was decreased by 27%. The difference in estimated total runoff was 11.8% depending on irrigation. As shown in this study, if grass management and irrigation are well-controlled, water quality of downstream areas could be obtained.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.182-182
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• 2020

최근 지구 온난화나 기상이변으로 인해 세계각지에서 많은 자연재해가 발생하고 있고 우리나라도 최근 전국 각지에서 국지성호우에 의한 많은 피해가 발생하고 있다. 특히 국지성호우로 인해 발생하는 산간지역의 토석류는 많은 재산피해를 일으키고 있다. 최근 토석, 토사, 혹은 부유 잡목 등의 유출로 인한 피해를 막기 위해 많은 사방댐을 축조하고 있으나 표면침식에 의해서 유출되는 토석량 혹은 토사량을 정확히 예측하지 못한다면 축조된 사방댐은 금방 제구실을 못할 수 있거나 혹은 과대 설계 및 시공되어 건설비를 낭비할 수 있다. 따라서 최적의 사방댐 건설을 위해 정확한 토석량의 산정은 매우 중요한 전제조건이라 할 수 있다. 본 연구에서는 강원도 인제군 산간지역 4곳의 사방댐유역에 대해 토석량 예측모형 MSDPM(Multi-Sequence Debris Prediction Model)과 LADMP(Los Angeles District Method for Prediction of sediments yield)를 이용하여 산정한 토석량과 실제 준설량을 비교하였다. 이를 위해 강원 산간지역에 맞도록 예측모형을 보정하였으며 토석류 유발 강우강도(Threshold Maximum 1-hr Rainfall Intensity)와 토석류 유발 최소강우량(Total Minimum Rainfall Amount)개념을 도입하여 예측모형식을 적용하였다. 위 식이 갖고 있는 대표적 특징 중 하나인 산불계수를 사용해야 하지만 본 연구지역은 산불 피해규모가 미미하여 산불의 영향은 고려하지 않고 토석량을 산정하였다. 두 예측모형의 계산결과와 실제 준설량을 비교해본 결과, MSDPM의 결과가 LADMP의 결과보다 준설량과 더 일치하는 것으로 나타났다. 실제 준설량과 MSDPM의 계산결과는 평균 17.37%의 차이를 나타냈고 LADMP의 계산결과는 평균 41.87%의 차이를 나타냈다. 본 연구에서 사용된 토석량 예측 모형은 앞으로 많은 산지유역의 토석량 예측에 사용이 가능 할 것으로 판단된다. 하지만 본 연구에서 사용된 자료의 제한성 때문에 앞으로 많은 실측 준설자료를 통하여 예측모형식을 보정하는 작업이 우선되어야 할 것으로 판단된다. 이를 위해서 많은 산지유역의 토석량을 장시간 실측하여 데이터를 축적하고 이를 사용하여 다양한 토석량 예측모형을 검보정하는 노력이 필요할 것으로 판단된다.

• 한국관개배수논문집
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• 제21권1호
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• pp.89-98
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• 2014

This study is to evaluate the future potential impact of climate change on soil erosion loss in a metropolitan area using Revised Universal Soil Loss Equation(RUSLE) with land use information of the Ministry of Environment and rainfall data for present and future years(30-year period). The spatial distribution map of vulnerable areas to soil erosion was prepared to provide the basis information for soil conservation and long-term land use planning. For the future climate change scenario, the MIROC3.2 HiRes A1B($CO_2720ppm$ level 2100) was downscaled for 2040-2069(2040s) and 2070-2099(2080s) using the stochastic weather generator(LARS-WG) with average rainfall data during past 30 years(1980-2010, baseline period). By applying the climate prediction to the RUSLE, the soil erosion loss was evaluated. From the results, the soil erosion loss showed a general tendency to increase with rainfall intensity. The soil loss increased up to 13.7%(55.7 ton/ha/yr) in the 2040s and 29.8%(63.6 ton/ha/yr) in the 2080s based on the baseline data(49.0 ton/ha/yr).

• 한국농공학회논문집
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• 제65권1호
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• pp.1-13
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• 2023

Various techniques for bias correction and statistical downscaling have been developed to overcome the limitations related to the spatial and temporal resolution and error of climate change scenario data required in various applied research fields including agriculture and water resources. In this study, the characteristics of three different statistical dowscaling methods (i.e., SQM, SDQDM, and BCSA) provided by AIMS were summarized, and climate change scenarios produced by applying each method were comparatively evaluated. In order to compare the average rainfall characteristics of the past period, an index representing the average rainfall characteristics was used, and the reproducibility of extreme weather conditions was evaluated through the abnormal climate-related index. The reproducibility comparison of spatial distribution and variability was compared through variogram and pattern identification of spatial distribution using the average value of the index of the past period. For temporal reproducibility comparison, the raw data and each detailing technique were compared using the transition probability. The results of the study are presented by quantitatively evaluating the strengths and weaknesses of each method. Through comparison of statistical techniques, we expect that the strengths and weaknesses of each detailing technique can be represented, and the most appropriate statistical detailing technique can be advised for the relevant research.

• 농업과학연구
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• 제50권4호
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• pp.799-808
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• 2023

Globally, abnormal climate phenomena have led to an increase in rainfall intensity, consequently causing a rise in flooding-related damages. Agricultural areas, in particular, experience significant annual losses every year due to a lack of research on flooding in these regions. This study presents a comprehensive analysis of the flood event that occurred on July 16, 2017, in the agricultural area situated in Sindaedong, Heungdeok-gu, Cheongju-si. To achieve this, the EPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) was employed to generate runoff data by rainfall information. The produced runoff data facilitated the identification of flood occurrence points, and the analysis results exhibited a strong correlation with inundation trace maps provided by the Ministry of the Interior and Safety (MOIS). The detailed output of the SWMM model enabled the extraction of time-specific runoff information at each inundation point, allowing for a detailed understanding of the inundation status in the agricultural area over different time frames. This research underscores the significance of utilizing the SWMM model to simulate inundation in agricultural areas, thereby validating the efficacy of flood alerts and risk management plans. In particular, the integration of rainfall data and the SWMM model in flood prediction methodologies is expected to enhance the formulation of preventative measures and response strategies against flood damages in agricultural areas.