• Atmosphere
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• v.11 no.2
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• pp.6-12
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• 2001

The East Asian monsoon is generally accompanied with the quasi-stationary front along the northern and northwestern periphery of the subtropical anticyclone in the boundary zone of the polar cold air mass and the tropical warm air mass. The rainy season in Korea has been called as Changma since the middle of 1500s. In meteorology, the rainy season with the quasi-stationary front, the Changma front, during the early summer has been defined as the Changma since 1905. The difference of meaning on Changma between meteorologists and the general public sometime does give a confusion. For example, the heavy rainfall event after the retreat of Changma is recognized as Changma by the general public, but not by most of meteorologists. The decision of the onset and retreat dates of Changma among the meteorologists is also ambiguous because of different viewpoints on the definition of Changma. In this study we survey the etymology and definition of Changma.

• Journal of Korea Water Resources Association
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• v.42 no.3
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• pp.213-225
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• 2009

A stationary Markov chain model is a stochastic process with the Markov property. Having the Markov property means that, given the present state, future states are independent of the past states. The Markov chain model has been widely used for water resources design as a main tool. A main assumption of the stationary Markov model is that statistical properties remain the same for all times. Hence, the stationary Markov chain model basically can not consider the changes of mean or variance. In this regard, a primary objective of this study is to develop a model which is able to make use of exogenous variables. The regression based link functions are employed to dynamically update model parameters given the exogenous variables, and the model parameters are estimated by canonical correlation analysis. The proposed model is applied to daily rainfall series at Seoul station having 46 years data from 1961 to 2006. The model shows a capability to reproduce daily and seasonal characteristics simultaneously. Therefore, the proposed model can be used as a short or mid-term prediction tool if elaborate GCM forecasts are used as a predictor. Also, the nonstationary Markov chain model can be applied to climate change studies if GCM based climate change scenarios are provided as inputs.

• Journal of Fisheries and Marine Sciences Education
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• v.27 no.6
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• pp.1755-1763
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• 2015

The objective of this study is to empirically analyze the relationship between sea temperature and jellyfish blooms. Ever since the 2000s, jellyfish population has been dramatically increased, which brought negative influence on the national health and the fisheries activities. Jellyfish blooms have been recognized as an effect of climate change, but there has been no empirical evidence to support such relationship. In this paper, the relationship between sea temperature and jellyfish blooms has been analyzed by using the regional jellyfish monitoring data and coastal stationary observing data of National Institute of Fisheries Science. Since the dependant variable carries left censoring issues, we used the panel tobit model. Our results indicate that there are statistically significant positive relationship between sea temperature and jellyfish blooms.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1437-1440
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• 2007

Many hydroclimatic time series are marked by interannual and longer quasi-period features that are associated with narrow band oscillatory climate modes. A time series modeling approach that directly considers such structures is developed and presented. The essence of the approach is to first develop a wavelet decomposition of the time series that retains only the statistically significant wavelet components, and to then model each such component and the residual time series as univariate autoregressive processes. The efficacy of this approach is demonstrated through the simulation of observed and paleo reconstructions of climate indices related to ENSO and AMO, tree ring and rainfall time series. Long ensemble simulations that preserve the spectral attributes of the time series in each ensemble member can be generated. The usual low order statistics are preserved by the proposed model, and its long memory performance is superior to the direction application of an autoregressive model.

• Journal of the Korean Professional Engineers Association
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• v.44 no.2
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• pp.23-28
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• 2011

The assumption that the spatiotemporal distribution of rainfall has stationarity for a long period is not realistic due to frequent unusual weather phenomena. Based on the understanding of the situation, this paper investigates the effects of it to hydraulic structures especially dams and deals measures for it.

• The Journal of Fisheries Business Administration
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• v.46 no.2
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• pp.31-41
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• 2015

This study aimed to analyze the relationship between sea surface temperature as a climatic element and catch amount of offshore and coastal fisheries in Korea using annual time series data from 1970 to 2013. It also tried to predict the future changes in catch amount of fisheries by climate change. Time series data on variables were estimated to be non-stationary from unit root tests, but one long-term equilibrium relation between variables was found from a cointegration test. The result of Granger causality test indicated that the sea surface temperature would cause directly changes in catch amount of offshore and coastal fisheries. The result of regression analysis on sea surface temperature and catch amount showed that the sea surface temperature would have negative impacts on the catch amount of offshore and coastal fisheries. Therefore, if the sea surface temperature would increase, all other things including the current level of fishing effort being equal, the catch amount of offshore and coastal fisheries was predicted to decrease.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.833-842
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• 2013

The quantile mapping is utilized to reproduce reliable GCM(Global Climate Model) data by correct systematic biases included in the original data set. This scheme, in general, projects the Cumulative Distribution Function (CDF) of the underlying data set into the target CDF assuming that parameters of target distribution function is stationary. Therefore, the application of stationary quantile mapping for nonstationary long-term time series data of future precipitation scenario computed by GCM can show biased projection. In this research the Nonstationary Quantile Mapping (NSQM) scheme was suggested for bias correction of nonstationary long-term time series data. The proposed scheme uses the statistical parameters with nonstationary long-term trends. The Gamma distribution was assumed for the object and target probability distribution. As the climate change scenario, the 20C3M(baseline scenario) and SRES A2 scenario (projection scenario) of CGCM3.1/T63 model from CCCma (Canadian Centre for Climate modeling and analysis) were utilized. The precipitation data were collected from 10 rain gauge stations in the Han-river basin. In order to consider seasonal characteristics, the study was performed separately for the flood (June~October) and nonflood (November~May) seasons. The periods for baseline and projection scenario were set as 1973~2000 and 2011~2100, respectively. This study evaluated the performance of NSQM by experimenting various ways of setting parameters of target distribution. The projection scenarios were shown for 3 different periods of FF scenario (Foreseeable Future Scenario, 2011~2040 yr), MF scenario (Mid-term Future Scenario, 2041~2070 yr), LF scenario (Long-term Future Scenario, 2071~2100 yr). The trend test for the annual precipitation projection using NSQM shows 330.1 mm (25.2%), 564.5 mm (43.1%), and 634.3 mm (48.5%) increase for FF, MF, and LF scenarios, respectively. The application of stationary scheme shows overestimated projection for FF scenario and underestimated projection for LF scenario. This problem could be improved by applying nonstationary quantile mapping.

• Environmental Analysis Health and Toxicology
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• v.31
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• pp.3.1-3.11
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• 2016

Objectives This study was conducted to describe the regional malaria incidence in relation to the geographic and climatic conditions and describe the effect of altitude on the expansion of malaria over the last decade in Papua New Guinea. Methods Malaria incidence was estimated in five provinces from 1996 to 2008 using national health surveillance data. Time trend of malaria incidence was compared with rainfall and minimum/maximum temperature. In the Eastern Highland Province, time trend of malaria incidence over the study period was stratified by altitude. Spatio-temporal pattern of malaria was analyzed. Results Nationwide, malaria incidence was stationary. Regionally, the incidence increased markedly in the highland region (292.0/100000/yr, p =0.021), and remained stationary in the other regions. Seasonality of the malaria incidence was related with rainfall. Decreasing incidence of malaria was associated with decreasing rainfall in the southern coastal region, whereas it was not evident in the northern coastal region. In the Eastern Highland Province, malaria incidence increased in areas below 1700 m, with the rate of increase being steeper at higher altitudes. Conclusions Increasing trend of malaria incidence was prominent in the highland region of Papua New Guinea, while long-term trend was dependent upon baseline level of rainfall in coastal regions.

• Journal of Wetlands Research
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• v.10 no.3
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• pp.125-132
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• 2008

Generally, stationary is considered as a basic assumption in frequency analysis. However, rainfall and flood discharge are changing due to the climate change and climate variability. Therefore, there is a new opinion that changing pattern of rainfall and flood discharge must be considered in frequency analysis. This study suggests the flood frequency analysis methodology using SIR algorithm which was developed from bootstrap could be used for considering climate change. Than is, SIR algorithm is selected for resampling method considering changing pattern of flood discharge and it has been used for resampling method with likelihood function. Resampled flood discharge data considering the increase of flood discharge pattern are used for parametric flood frequency analysis and this results are compared with frequency analysis results by Bootstrap and original observations. As the results, SIR algorithm shows the greatest flood discharge than other methods in all frequencies and this may reflect the increasing pattern of flood discharge due to the climate change and climate variability.

• Journal of Korean Society on Water Environment
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• v.33 no.3
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• pp.256-272
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• 2017

Extreme rainfall has become more frequent over the Korean peninsula in recent years, causing serious damages. In a changing climate, traditional approaches based on historical records of rainfall and on the stationary assumption can be inadequate and lead to overestimate (or underestimate) the design rainfalls. A main objective of this study is to develop a stochastic disaggregation method of seasonal rainfall to hourly extreme rainfall, and offer a way to derive the nonstationary IDF curves. In this study, we propose a novel approach based on a Four-Parameter Beta (4P-beta) distribution to estimate the nonstationary IDF curves conditioned on the observed (or simulated) seasonal rainfall, which becomes the time-varying upper bound of the 4P beta distribution. Moreover, this study employed a Bayesian framework that provides a better way to take into account the uncertainty in the model parameters. The proposed model showed a comparable design rainfall to that of GEV distribution under the stationary assumption. As a nonstationary rainfall frequency model, the proposed model can effectively translate the seasonal variation into the sub-daily extreme rainfall.