기후변화는 홍수의 가장 큰 원인이 되는 극치강우의 빈도와 크기에 매우 큰 영향을 미치고 있다. 특히, 우리나라에서 발생하는 대규모 재해는 강우에 의한 홍수피해가 대부분을 차지하고 있다. 이러한 홍수피해는 기후변화에 의한 극한강우의 발생 빈도가 높아짐에 따라 새로운 재해양상으로 전개되고 있다. 하지만, 미래 기후변화 시나리오 자료는 해상도의 한계로 인하여 중소규모 하천 및 도시유역에 요구되는 수준의 자료 수집이 불가능한 상태이다. 이러한 문제점을 개선하기 위하여 본 연구에서는 전지구모형에서 생산된 기후변화 시나리오에 대해서 여러 단계의 통계적 상세화 기법을 통하여 우리나라 전역에 대하여 미래 시나리오에 대한 빈도해석이 가능하도록 각 지점의 특성에 따라 시간적으로 상세화하기 위해 개발된 방 법 및 과정을 소개하였다. 이를 통해, 시간상세화 자료를 토대로 미래 강우에 대한 빈도해석과 기후변화에 따른 방재성능 목표강우량을 산정하는데 활용할 수 있도록 하였다.

The ordinary least square method (OLS) has been the most frequently used least square method in hydrological data analysis. Its computational algorithm is simple, and the error analysis is also simple and clear. However, the primary assumption of the OLS method, which states that the dependent variable is the only error-contaminated variable and all other variables are error free, is often violated in hydrological data analyses. Recently, a matrix algorithm using the singular value decomposition for the total least square (TLS) method has been developed and used in data analyses as errors-in-variables model where several variables could be contaminated with observational errors. In our study, the algorithm of the TLS is introduced in the evaluation of rating curves between the flow discharge and the water level. Then, the TLS algorithm is applied to real data set for rating curves. The evaluated TLS rating curves are compared with the OLS rating curves, and the result indicates that the TLS rating curve and the OLS rating curve are in good agreement. The TLS and OLS rating curves are discussed about their algorithms and error terms in the study.

This study proposes a new parameter estimation approach for the mixture normal distribution. The developed model estimates parameters of the mixture normal distribution by maximizing the log likelihood function using a meta-heuristic algorithm-genetic algorithm (GA). To verify the performance of the developed model, simulation experiments and practical applications are implemented. From the results of experiments and practical applications, the developed model presents some advantages, such as (1) the proposed model more accurately estimates the parameters even with small sample sizes compared to the expectation maximization (EM) algorithm; (2) not diverging in all application; and (3) showing smaller root mean squared error and larger log likelihood than those of the EM algorithm. We conclude that the proposed model is a good alternative in estimating the parameters of the mixture normal distribution for kutotic and bimodal hydrometeorological data.

The objective of the current study is to compare the performances of a classical regression method (SWR) and the LASSO technique for predictor selection. A data set from 9 stations located in the southern region of Quebec that includes 25 predictors measured over 29 years (from 1961 to 1990) is employed. The results indicate that, due to its computational advantages and its ease of implementation, the LASSO technique performs better than SWR and gives better results according to the determination coefficient and the RMSE as parameters forcomparison.

We developed a stochastic model that captures long term nonstationary oscillations (NSOs) within a given variable. The model employs a data-adaptive decomposition method named empirical mode decomposition (EMD). Irregular oscillatory processes in a given variable can be extracted into a finite number of intrinsic mode functions with the EMD approach. A unique data-adaptive algorithm is proposed in the present paper in order to study the future evolution of the NSO components extracted from EMD. To evaluate the model performance, the model is tested with the synthetic data set from Rossler attractor and with global surface temperature anomalies (GSTA) data. The results of the attractor show that the proposed approach provides a good characterization of the NSOs. For GSTA data, the last 30 observations are truncated and compared to the generated data. Then the model is used to predict the evolution of GSTA data over the next 50 years. The results of the case study confirm the power of the EMD approach and the proposed NSO resampling (NSOR) method as well as their potential for the study of climate variables.

Reproducing nonstationary oscillation (NSO) processes in a stochastic time series model is a difficult task because of the complexity of the nonstationary behaviors. In the current study, a novel stochastic simulation technique that reproduces the NSO processes embedded in hydroclimatic data series is presented. The proposed model reproduces NSO processes by utilizing empirical mode decomposition (EMD) and nonparametric simulation techniques (i.e., k-nearestneighbor resampling and block bootstrapping). The model was first tested with synthetic data sets from trigonometric functions and the Rossler system. The North Atlantic Oscillation (NAO) index was then examined as a real case study. This NAO index was then employed as an exogenous variable for the stochastic simulation of streamflows at the Romaine River in the province of Quebec, Canada. The results of the application to the synthetic data sets and the real-world case studies indicate that the proposed model preserves well the NSO processes along with the key statistical characteristics of the observations. It was concluded that the proposed model possesses a reasonable simulation capacity and a high potential as a stochastic model, especially for hydroclimatic data sets that embed NSO processes.

Climate indices generally contain nonstationary oscillations (NSO). Not much study has been done in the literature to reproduce the NSO processes through a stochastic time series model. Therefore, we proposed a model that reproduces the NSO of climate indices employing EMD- NSO resampling (NSOR) technique. The proposed simulation model was tested with three climate indices (i.e. AO, ENSO, and PDO) for the annual and winter (January, February, and March - JFM) datasets. The results of the proposed model are compared with the ones of the Contemporaneous Shifting Mean and Contemporaneous Autoregressive Moving Average (CSM-CARMA) model. One set of the 5000 year records is simulated from each model. The results (ex. Figure 1) indicated that the proposed model is superior to the CSM-CARMA model for reproducing the NSO process while the other basic statistics are comparatively well preserved in both models.

Hydrologic responses to variations in storm direction provide useful information for the analysis and prediction of floods and the development of watershed management strategies. However, the prediction of hydrologic responses to changes in storm direction is a difficult task that requires meteorological simulations and extensive computation. It is also difficult to identify the center of rotation of a storm affecting a basin of interest. Therefore, we propose a simple approach of rotating the basin position relative to the storm within the rainfall-runoff simulation model instead of changing the pathway of the storm, which we term the Basin Rotation Method (BRM). The proposed BRM was tested on four major typhoon events in South Korea. The results illustrated that the original basin orientation (i.e., before it was rotated) exhibits earlier and higher peak discharge and earlier recession compared to the basin after rotation. We conclude that the proposed method (BRM) is a viable alternative for use in assessing the directional influence of moving storms on floods caused by historical rather than hypothetical storm events.

We illustrate in the current study that fitting a univariate time series model to each extracted component might end up with the underestimation of the serial dependence that the observation data might contain. A alternative for parameter estimation is suggested to preserve the serial dependence of the observation variable using the relationship between the observation variable and the decomposed variable. The case study of the Upper Colorado River basin shows that some improvement is made through the suggested alternative.

Radars have been widely employed to detect precipitation and to predict rainfall. However, the radar-based estimate of rainfall is affected by uncertainties or errors such as mis-calibration, beam blockage, anomalous propagation, and ground cutter. Even though these uncertainties of radar rainfall estimate (RRE) have been studied, their effect on a runoff simulation especially to the peak discharge and peak time have not been much focused. Therefore, the objective of current study is to analyze the effect of the RRE uncertainties or errors based on synthetic simulation of RRE and its effect on peak discharge. First of all, mean of modeled radar rainfall is fixed (e.g., 100mm) and its error variance was set as ±10mm, ±20mm, ±40mm, and ±50mm independent to each grid cell. This independent simulation is based on white-noise process. The second simulation included a spatial-correlation between grid cells in simulating the error variance. The relationship between the distances of rain gauges and the corresponding correlations was modeled with the power law function. The parameters of the function were estimated through meta-heuristic method (specifically harmony search). Moreover, in order to find the correlation of observed data, the whole data from 27 rain gauges in the basin and the corresponding RRE from the dual polarization radar on Mt. Bisl in Korea were employed. The results of the former simulation (independent errors to each grid cell) show that the bias of the peak discharge is increased along with the variance increased, which is caused by influence of zero values. In the latter simulation (spatially correlated errors between grid cells), the results show that the peak discharge variance from the latter presents much larger than that of the former. Furthermore, the spatial distribution pattern of the modeled radar rainfall exhibited very similar to that of the real rainfall. Finally, we concluded that the error variance of RRE on runoff simulation leading bias and high uncertainty.

In the current study, a temporal downscaling model that combines a nonparametric stochastic simulation approach with a genetic algorithm is proposed. The proposed model was applied to Jinju station in South Korea for a historical time period to validate the model performance. The results revealed that the proposed model preserves the key statistics (i.e., the mean, standard deviation, skewness, lag-1 correlation, and maximum) of the historical hourly precipitation data. In addition, the occurrence and transition probabilities are well preserved in the downscaled hourly precipitation data. Furthermore, the RCP4.5 and RCP8.5 climate scenarios for the Jinju station were also analyzed, revealing that the mean and the wet-hour probability significantly increased and the standard deviation and maximum slightly increased in these scenarios. The magnitude of the increase was greater in RCP8.5 than RCP4.5.

The probability distribution of wind speeds is a mathematical function describing the range and relative frequency of wind speeds at a particular location . In other word , the behavior of wind velocity at a given site can be specified as a probability distribution function. The accuracy of design wind estimation depends on the choice of an appropriate probability distribution model (PDM) and parameter estimation techniques. Generally, parameters for PDMs are estimated with the method of moments(MOM), probability weighted moments(PWM), and maximum likelihood (ML). In this work , we tried to estimate the parameters of PDMs for wind speed data using a recently developed meta-heuristic approach known as a harmony search (HS) that is a phenomenon-mimicking algorithm. The performance of the HS is compared to the genetic algorithm (GA) and conventional method (i.e.,ML) via simulation and case study.