• Journal of Korean Society of Water and Wastewater
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• v.26 no.2
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• pp.321-329
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• 2012

In this study, statistical analysis under both stationary and non-stationary climate was conducted for rainfall data measured in Seoul. Generalised Extreme Value (GEV) distribution and Gumbel distribution were used for the analysis. Rainfall changes under the non-stationary climate were estimated by applying time variable (t) to location parameter (${\xi}$). Rainfall depths calculated in non-stationary climate increased by 1.1 to 6.2mm and 1.0 to 4.6mm for the GEV distribution and gumbel distribution respectively from those stationary forms. Changes in annual maximum rainfall were estimated with rate of change in the location parameter (${\xi}1{\cdot}t$), and temporal changes of return period were predicted. This was also available for re-evaluating the current sewer design return period. Design criteria of sewer system was newly suggested considering life expectance of the system as well as temporal changes in the return period.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.499-514
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• 2011

An underlying assumption of traditional hydrologic frequency analysis is that climate, and hence the frequency of hydrologic events, is stationary, or unchanging over time. Under stationary conditions, the distribution of the variable of interest is invariant to temporal translation. Water resources infrastructure planning and design, such as dams, levees, canals, bridges, and culverts, relies on an understanding of past conditions and projection of future conditions. But, Water managers have always known our world is inherently non-stationary, and they routinely deal with this in management and planning. The aim of this paper is to give a brief introduction to non-stationary extreme value analysis methods. In this paper, a non-stationary hydrologic frequency analysis approach is introduced in order to determine probability rainfall consider changing climate. The non-stationary statistical approach is based on the conditional Generalized Extreme Value(GEV) distribution and Maximum Likelihood parameter estimation. This method are applied to the annual maximum 24 hours-rainfall. The results show that the non-stationary GEV approach is suitable for determining probability rainfall for changing climate, sucha sa trend, Moreover, Non-stationary frequency analyzed using SOI(Southern Oscillation Index) of ENSO(El Nino Southern Oscillation).

• Journal of Korean Society of Water and Wastewater
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• v.30 no.1
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• pp.51-58
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• 2016

On account of the increase in water demand and climate change, droughts are in great concern for water resources planning and management. In this study, rainfall characteristics with stationary and non-stationary perspectives were analyzed using Weibull distribution model with 40-year records of annual minimum rainfall depth collected in major cities of Korea. As a result, the non-stationary minimum probable rainfall was expected to decrease, compared with the stationary probable rainfall. The reliability of ${\xi}_1$, a variable reflecting the decrease of the minimum rainfall depth due to climate change, in Wonju, Daegu, and Busan was over 90%, indicating the probability that the minimal rainfall depths in those city decrease is high.

• Wind and Structures
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• v.26 no.3
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• pp.129-146
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• 2018

Extreme wind speed analysis has been carried out conventionally by assuming the extreme series data is stationary. However, time-varying trends of the extreme wind speed series could be detected at many surface meteorological stations in China. Two main reasons, exposure change and climate change, were provided to explain the temporal trends of daily maximum wind speed and annual maximum wind speed series data, recorded at Hangzhou (China) meteorological station. After making a correction on wind speed series for time varying exposure, it is necessary to perform non-stationary statistical modeling on the corrected extreme wind speed data series in addition to the classical extreme value analysis. The generalized extreme value (GEV) distribution with time-dependent location and scale parameters was selected as a non-stationary model to describe the corrected extreme wind speed series. The obtained non-stationary extreme value models were then used to estimate the non-stationary extreme wind speed quantiles with various mean recurrence intervals (MRIs) considering changing climate, and compared to the corresponding stationary ones with various MRIs for the Hangzhou area in China. The results indicate that the non-stationary property or dependence of extreme wind speed data should be carefully evaluated and reflected in the determination of design wind speeds.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.1
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• pp.41-49
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• 2016

In this study, rainfall characteristics with stationary and non-stationary perspectives were analyzed using generalized extreme value (GEV) distribution and Gumbel distribution models with rainfall data collected in major cities of Korea to reevaluate the return period of sewer flooding in those cities. As a result, the probable rainfall for GEV and Gumbel distribution in non-stationary state both increased with time(t), compared to the stationary probable rainfall. Considering the reliability of ${\xi}_1$, a variable reflecting the increase of storm events due to climate change, the reliability of the rainfall duration for Seoul, Daegu, and Gwangju in the GEV distribution was over 90%, indicating that the probability of rainfall increase was high. As for the Gumbel distribution, Wonju, Daegu, and Gwangju showed the higher reliability while Daejeon showed the lower reliability than the other cities. In addition, application of the maximum annual rainfall change rate (${\xi}_1{\cdot}t$) to the location parameter made possible the prediction of return period by time, therefore leading to the evaluation of design recurrence interval.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.611-622
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• 2018

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

• Magazine of the Korea Concrete Institute
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• v.9 no.3
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• pp.157-166
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• 1997

The object of this study was invest, igat, ing the failure phenomenon in the contact zone of rcpnired concrete structures due to the external climate change(hygral transient condition). This study was carrie out by calculating the non-stationary moisture and stress distribution in the repaired concrete structures with the cement mortar. In this analysis, main variables were the overlay thickness (Do=0.5-2.5cm). and the pre-wetting time(tc= l-5days). and the cxtcrnal 1.~1ative humidity(Ho=50-80%). The results show that the minimum overlay thickness and the minimum pre-wetting time are necessary to k e ~ p compressive stresses in the contact zone for a relative humidity.

• Journal of Coastal Disaster Prevention
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• v.4 no.3
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• pp.119-131
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• 2017

Global warming under the influence of climate change and its direct impact on glacial and sea level are known issue. However, there is a lack of research on an indirect impact of climate change such as coastal structure design which is mainly based on a frequency analysis of water level under the stationary assumption, meaning that maximum sea level will not vary significantly over time. In general, stationary assumption does not hold and may not be valid under a changing climate. Therefore, this study aims to develop a novel approach to explore possible distributional changes in annual maximum sea levels (AMSLs) and provide the estimate of design water level for coastal structures using a multiple non-crossing quantile regression based nonstationary frequency analysis within a Bayesian framework. In this study, 20 tide gauge stations, where more than 30 years of hourly records are available, are considered. First, the possible distributional changes in the AMSLs are explored, focusing on the change in the scale and location parameter of the probability distributions. The most of the AMSLs are found to be upward-convergent/divergent pattern in the distribution, and the significance test on distributional changes is then performed. In this study, we confirm that a stationary assumption under the current climate characteristic may lead to underestimation of the design sea level, which results in increase in the failure risk in coastal structures. A detailed discussion on the role of the distribution changes for design water level is provided.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.5
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• pp.139-152
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• 2015

Along with climate change, it is reported that the scale and frequency of extreme climate events show unstable tendency of increase. Thus, to comprehend the change characteristics of precipitation data, it is needed to consider non-stationary. The main objectives of this study were to estimate future design floods for Wonpyeongcheon watershed based on RCP (Representative Concentration Pathways) scenario. Wonpyeongcheon located in the Keum River watershed was selected as the study area. Historical precipitation data of the past 35 years (1976~2010) were collected from the Jeonju meteorological station. Future precipitation data based on RCP4.5 were also obtained for the period of 2011~2100. Systematic bias between observed and simulated data were corrected using the quantile mapping (QM) method. The parameters for the bias-correction were estimated by non-parametric method. A non-stationary frequency analysis was conducted with moving average method which derives change characteristics of generalized extreme value (GEV) distribution parameters. Design floods for different durations and frequencies were estimated using rational formula. As the result, the GEV parameters (location and scale) showed an upward tendency indicating the increase of quantity and fluctuation of an extreme precipitation in the future. The probable rainfall and design flood based on non-stationarity showed higher values than those of stationarity assumption by 1.2%~54.9% and 3.6%~54.9%, respectively, thus empathizing the necessity of non-stationary frequency analysis. The study findings are expected to be used as a basis to analyze the impacts of climate change and to reconsider the future design criteria of Wonpyeongcheon watershed.

• Journal of Korea Water Resources Association
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• v.48 no.6
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• pp.451-461
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• 2015

This study aimed to estimate the future design rainfall through a non-stationary frequency analysis using the rainfall separation technique. First, we classified rainfall in the Korean Peninsula into local downpour and TC-induced rainfall through rainfall separation technique based on the path and size of a typhoon. Furthermore, we performed the analysis of regional rainfall characteristics and trends. In addition, we estimated the future design rainfall through a non-stationary frequency analysis using Gumbel distribution and carried out its quantitative comparison and evaluation. The results of the analysis suggest that the increase and decrease rate of rainfall in the Korean Peninsula were different and the increasing and decreasing tendencies were mutually contradictory at some points. In addition, a non-stationary frequency analysis was carried out by using the rainfall separation technique. The outcome of this analysis suggests that a relatively reasonable future design rainfall can be estimated. Comparing total rainfall with the future design rainfall, differences were found in the southern and eastern regions of the Korean peninsula. This means that climate change may have a different effect on the typhoon and local downpour. Thus, in the future, individual assessment of climate change impacts needs to be done through moisture separation. The results presented here are applicable in future hydraulic structures design, flood control measures related to climate change, and policy establishment.