• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.326-326
• /
• 2022

기후 변화 시나리오는 온실가스, 에어로졸, 토지이용 변화 등 인위적인 원인으로 발생한 복사강제력 변화를 지구시스템 모델에 적용하여 산출한 미래 기후 전망정보(기온, 강수량, 바람, 습도 등)를 생산하는데 활용된다. 또한, 미래에 기후변화로 인한 영향을 평가하고 피해를 최소화하는데 활용할 수 있는 선제적인 정보로 활용된다. GCM과 RCM은 구조 및 모수화 과정, 불확실성 등의 한계로 인하여 상대적으로 큰 시공간적 규모를 가지며, 실제 관측된 기상인자들을 재현하는데 시공간적 차이 즉 편의(bias)가 발생하며. 실제 관측된 기상인자의 시간적 변화 특성을 재현하지 못하는 문제점을 내재하고 있는 것으로 보고되고 있다. 이러한 점에서 기후모델에서 생산된 정보를 수문학적으로 적용하기 위해서는 시공간적 상세화와 편의 보정은 필수적이다. 본 연구에서는 관측자료를 사용하여 재해석 자료를 편의보정 한 뒤. 기후 변화 시나리오를 합성곱 신경망(CNN)을 기반으로 상세화 과정을 진행하여 고해상도 자료를 생산하였으며, CNN 기반 상세화 기법 적용성은 지상 관측자료 대상으로 평가하였다.

• The Journal of Engineering Geology
• /
• v.9 no.2
• /
• pp.147-159
• /
• 1999

Geostatistical methods were used for the groundwater flow analysis in a heterogeneous anisotropic aquifer. This study area is located at Sonbul-myeon in Hampyong-gun of Cheonnam Province which is a hydrogeological project area of KORES(Korea Resources Cooperation). Linear regression analysis shows that the topographic elevation and groundwater level of this area have very high correlation. Groundwater-level contour maps produced by ordinary kriging and cokringing have large differences in mountain areas, but small differences in hill and plain areas near the West Sea. Comparing two maps on the basis of an elevation contour map, a groundwater-level contour map using cokriging is more accurate. Analyzing the groundwater flow on two groundwater-level contour maps, the groundwater of study area flows from the high mountain areas to the plain areas near the West Sea. To verify the enffectiveness of geostatistical methods for the groundwater flow analysis in a heterogeneous anisotropic aquifer, the flow directions of groundwater were measured at two groundwater boreholes by a groundwater flowmeter system(model 200 $GeoFlo^{R}$). The measured flow directions of groundwater almost accord with those estimated on two groundwater-level contour maps produced by geostatistical methods.

• Journal of Korean Society of Disaster and Security
• /
• v.13 no.1
• /
• pp.1-11
• /
• 2020

One of the important problem in statistical hydrology is to estimate the appropriated probability distribution for a given sample data. For the problem, a goodness-of-fit test is conducted based on the similarity between estimated probability distribution and assumed theoretical probability distribution. Probability plot correlation coefficient test (PPCC) is one of the goodness-of-fit test method. PPCC has high rejection power and its application is simple. In this study, test statistics of PPCC were derived for generalized extreme value distribution (GEV) models based on L-moments and these statistics were suggested by the multiple and nonlinear regression equations for its usability. To review the rejection power of the newly proposed method in this study, Monte Carlo simulation was performed with other goodness-of-fit tests including the existing PPCC test. The results showed that PPCC-A test which is proposed in this study demonstrated better rejection power than other methods, including the existing PPCC test. It is expected that the new method will be helpful to estimate the appropriate probability distribution model.

• Journal of Korea Water Resources Association
• /
• v.39 no.8 s.169
• /
• pp.669-676
• /
• 2006

Hydrological factors, especially the spatial distribution of interpretation on precipitation is often topic of interest in studying of water resource. The popular methods such as Thiessen method, inverse distance method, and isohyetal method are limited in calculating the spatial continuity and geographical characteristics. This study was intended to overcome those limitations with improved method that will yield higher accuracy. The monthly and yearly precipitation data were produced and compared with the observed daily precipitation to find correlation between them. They were then used as secondary variables in Co-kriging method, and the result was compared with the outcome of existing methods like inverse distance method and kriging method. The comparison of the data showed that the daily precipitation had high correlation with corresponding year's average monthly amounts of precipitation and the observed average monthly amounts of precipitation. Then the result from the application of these data for a Co-kriging method confirmed increased accuracy in the modeling of spatial distribution of precipitation, thus indirectly reducing inconsistency of the spatial distribution of hydrological factors other than precipitation.

• Journal of Korea Water Resources Association
• /
• v.38 no.12 s.161
• /
• pp.985-994
• /
• 2005

We examined problems of the principal component analysis(PCA), which is able to analyze at the low dimensionality as a methodologv to assess hydrologic time series, and introduced the theory and characteristics of independent component analysis(ICA) that can supplement problems of principal component analysis. We also applied the global sea surface temperature(SST) of the Nino region and assessed the correlation between El $\tilde{n}ino$-Southern Oscillation(ENSO) and SST. The results of examining separation-ability of principal components using mixed signals indicate that the independent component analysis is statistically superior compared to that of the principal component analysis. Finally, we assessed correlation between ENSO and global anomaly SST. The independent component analysis was applied to the $5^{\circ}{\times}5^{\circ}$(latitude and longitude) global anomaly SST in the Nino+3.4 region that is the El $\tilde{n}ino$ observation section. We assessed the correlation with the ENSO years. These results of the analysis show that only one independent component($86\%$) was able to represent the entire behavior and was consistent with the main ENSO years. Finally, we carried out independent component analysis for summer seasonal rainfalls at nine stations and could extract ICs to reflect geographical characteristics. The increasing trend has been shown at IC-1 and IC-2 since 1970s.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.40 no.3
• /
• pp.303-314
• /
• 2020

The hydrological characteristics of watersheds and hydraulic systems of urban and river floods are highly nonlinear and contain uncertain variables. Therefore, the predicted time series of rainfall-runoff data in flood analysis is not suitable for existing neural networks. To overcome the challenge of prediction, a NARX (Nonlinear Autoregressive Exogenous Model), which is a kind of recurrent dynamic neural network that maximizes the learning ability of a neural network, was applied to forecast a flood in real-time. At the same time, NARX has the characteristics of a time-delay neural network. In this study, a hydrological model was constructed for the Taehwa river basin, and the NARX time-delay parameter was adjusted 10 to 120 minutes. As a result, we found that precise prediction is possible as the time-delay parameter was increased by confirming that the NSE increased from 0.530 to 0.988 and the RMSE decreased from 379.9 ㎥/s to 16.1 ㎥/s. The machine learning technique with NARX will contribute to the accurate prediction of flow rate with an unexpected extreme flood condition.

• Journal of Korea Water Resources Association
• /
• v.49 no.4
• /
• pp.335-346
• /
• 2016

This study produced the parameter maps of the Modified Bartlett-Lewis Rectangular Pulse (MBLRP) stochastic rainfall generation model across South Korea and developed and validated the web application that automates the process of rainfall generation based on the produced parameter maps. To achieve this purpose, three deferent sets of parameters of the MBLRP model were estimated at 62 ground gage locations in South Korea depending on the distinct purpose of the synthetic rainfall time series to be used in hydrologic modeling (i.e. flood modeling, runoff modeling, and general purpose). The estimated parameters were spatially interpolated using the Ordinary Kriging method to produce the parameter maps across South Korea. Then, a web application has been developed to automate the process of synthetic rainfall generation based on the parameter maps. For validation, the synthetic rainfall time series has been created using the web application and then various rainfall statistics including mean, variance, autocorrelation, probability of zero rainfall, extreme rainfall, extreme flood, and runoff depth were calculated, then these values were compared to the ones based on the observed rainfall time series. The mean, variance, autocorrelation, and probability of zero rainfall of the synthetic rainfall were similar to the ones of the observed rainfall while the extreme rainfall and extreme flood value were smaller than the ones derived from the observed rainfall by the degree of 16%-40%. Lastly, the web application developed in this study automates the entire process of synthetic rainfall generation, so we expect the application to be used in a variety of hydrologic analysis needing rainfall data.

• Journal of Wetlands Research
• /
• v.20 no.2
• /
• pp.105-115
• /
• 2018

The frequency analysis of hydrometeorological data is one of the most important factors in response to natural disaster damage, and design standards for a disaster prevention facilities. In case of frequency analysis of hydrometeorological data, it assumes that observation data have statistical stationarity, and a parametric method considering the parameter of probability distribution is applied. For a parametric method, it is necessary to sufficiently collect reliable data; however, snowfall observations are needed to compensate for insufficient data in Korea, because of reducing the number of days for snowfall observations and mean maximum daily snowfall depth due to climate change. In this study, we conducted the frequency analysis for snowfall using the Bootstrap method and SIR algorithm which are the resampling methods that can overcome the problems of insufficient data. For the 58 meteorological stations distributed evenly in Korea, the probability of snowfall depth was estimated by non-parametric frequency analysis using the maximum daily snowfall depth data. The results of frequency based snowfall depth show that most stations representing the rate of change were found to be consistent in both parametric and non-parametric frequency analysis. According to the results, observed data and Bootstrap method showed a difference of -19.2% to 3.9%, and the Bootstrap method and SIR(Sampling Importance Resampling) algorithm showed a difference of -7.7 to 137.8%. This study shows that the resampling methods can do the frequency analysis of the snowfall depth that has insufficient observed samples, which can be applied to interpretation of other natural disasters such as summer typhoons with seasonal characteristics.

• Journal of Korea Water Resources Association
• /
• v.48 no.10
• /
• pp.793-806
• /
• 2015

The simulation of natural streamflow at ungauged basins is one of the fundamental challenges in hydrology community. The key to runoff simulation in ungauged basins is generally involved with a reliable parameter estimation in a rainfall-runoff model. However, the parameter estimation of the rainfall-runoff model is a complex issue due to an insufficient hydrologic data. This study aims to regionalize the parameters of a continuous rainfall-runoff model in conjunction with a Bayesian statistical technique to consider uncertainty more precisely associated with the parameters. First, this study employed Bayesian Markov Chain Monte Carlo scheme for the estimation of the Sacramento rainfall-runoff model. The Sacramento model is calibrated against observed daily runoff data, and finally, the posterior density function of the parameters is derived. Second, we applied a multiple linear regression model to the set of the parameters with watershed characteristics, to obtain a functional relationship between pairs of variables. The proposed model was also validated with gauged watersheds in accordance with the efficiency criteria such as the Nash-Sutcliffe efficiency, index of agreement and the coefficient of correlation.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.590-590
• /
• 2015

본 연구는 집중호우에 의한 홍수예측 및 소유역의 유출거동에 대한 수문학적 민감성(susceptibility) 규명을 목적으로 강우강도, 지속기간 및 토양포화도 변화에 따른 홍수유출특성을 분석하여 유역의 유출거동 민감성을 표출할 수 있는 노모그래프를 개발하였다. 개별 홍수사상에 대한 유출거동 특성 분석을 위하여 한국건설기술연구원의 대표 시험유역인 설마천 유역의 과거 17년간(1996 ~ 2012)의 10분 간격의 강우량 및 유출량 자료를 수집하여 홍수유출해석을 수행하였다. 설마천 시험유역의 일누가강우량 100mm 이상, 50개 홍수사상에 대한 홍수유출해석은 유역 물순환 해석모형인 CAT(Catchment hydrological cycle Assessment Tool)을 이용하였으며 모의결과를 바탕으로 홍수사상별 지체시간, 강우강도, 지속기간 및 토양포화도 변화에 따른 홍수유출특성을 상세히 분석하였다. 이 중에서도 지체시간은 유역반응을 나타내는 시간변수로서 수문모델링 및 홍수량예측에 매우 중요한 요소이다. 특히, 강우량에 대한 홍수량의 반응이 빠른 소유역의 경우에 홍수량예측에 큰 영향을 미친다. 따라서 강우강도, 지속기간, 토양포화도의 변화량에 대한 지체시간의 거동을 R 프로그래밍 언어 및 3D Surfer를 이용하여 분석한 후 최종적으로 소유역의 홍수유출 특성을 나타내는 3차원 홍수 유출특성 노모그래프를 개발하였다. 분석에 사용된 R 프로그래밍 언어는 통계 계산과 그래픽을 위한 프로그래밍 언어이자 소프트웨어 환경으로 데이터의 조작 및 수치연산, 시각화를 수행할 수 있는 기능을 여러 패키지를 통해 구현할 수 있다. 따라서 본 연구에서는 R을 이용하여 10분 단위의 강우 및 유출량 자료를 1시간 및 1일 자료로 구축하고 17년간의 과거 홍수사상을 분리하여 추출하는 R 홍수유출해석 시스템을 개발하였으며 추출된 홍수사상을 관측 유출량 및 관측 토양수분을 포함하여 시각화함으로써 강우 및 토양수분 변화에 따른 소유역의 유출거동 민감성을 확인할 수 있었다. 분석 결과, 지체시간은 강우지속기간 및 토양포화도에 민감한 거동특성을 나타냈으며 토양포화도는 첨두홍수량의 변화에 민감한 영향을 주는 것으로 확인되었다. 개발된 3차원 홍수유출특성 노모그래프는 유역의 규모 및 지형물리학적 특성에 따라 다양하게 나타날 것으로 판단되며 여러 계측유역에 적용함으로써 유역별 홍수유출 반응특성을 정량화할 필요가 있다. 즉, 강우강도, 지속기간, 지체시간, 포화도 등의 변화에 따른 유역의 홍수유출 반응특성을 규명함으로써 미계측 유역의 홍수량예측 실무에 활용할 수 있을 것으로 판단된다.