• Title/Summary/Keyword: Gumbel model

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Estimation on Altitudinal Spectrum of Suitability for Four Species of the Mayfly Genus Ephemera (Ephemeroptera: Ephemeridae) Using Probability Distribution Models (확률분포모형을 이용한 하루살이속(Ephemera) 4종의 고도구배에 따른 서식처적합도 평가)

  • Dongsoo Kong;Bomi Kang
    • Journal of Korean Society on Water Environment
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    • v.39 no.4
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    • pp.302-315
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    • 2023
  • Distribution characteristics and altitudinal gradient of four species (E. strigata, E. separigata, E. orientalis-sachalinensis group) of the mayfly genus Ephemera (Order Ephemeroptera) were analyzed with probability distribution models (exponential, normal, lognormal, logistic, Weibull, gamma, beta, Gumbel). Data was collected from 23,846 sampling units of 6,787 sites in Korea from 2010 to 2021. The beta distribution model showed the best fit for positively skewed E. orientalis-sachalinensis and little-skewed E. strigata along with altitudinal gradient. The reversed lognormal distribution model showed the best-fit for negatively skewed E. separigata. E. orientalis-sachalinensis distributed at the range of altitude 1~700 m (mean 251 m, median 226 m, mode 124 m, and standard deviation 161 m), E. strigata distributed at the range of altitude 5~871 m (mean 474 m, median 478 m, mode 492 m, and standard deviation 200 m), E. separigata distributed at the range of altitude 7~846 m (mean 620 m, median 659 m, mode 760 m, and standard deviation 181 m). Altitudinal habitat suitability ranges were estimated to be 42~257 m for E. orientalis-sachalinensis, 335~644 m for E. strigata, and 641~824 m for E. separigata. Based on the altitudinal spectrum of suitability and altitude-related temperature analysis results, E. orientalis-sachalinensis was estimated to be thermophilic, E. strigata to be mesophilic, and E. separigata to be thermophobic. This is the first national-scale evaluation of the altitudinal distribution of Ephemera in Korea. These results will be used in a further research study on altitudinal shift of the species of Ephemera under climate change.

Reliability model for the probability of system failure of storm sewer (우수관의 불능확률 산정을 위한 신뢰성 모형)

  • Kwon, Hyuk-Jae;Lee, Cheol-Eung;Ahn, Jae-Beom
    • Proceedings of the Korea Water Resources Association Conference
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    • 2010.05a
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    • pp.1691-1695
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    • 2010
  • 본 연구에서 AFDA(Approximate Full Distribution Approach)를 사용하여 하수관의 불능확률을 정량적으로 산정할 수 있는 신뢰성 모형이 개발되었다. 여러 도시의 연 최대강우강도(Yearly Maximum Rainfall Intensity)를 이용하여 그 확률분포함수를 분석하였고 우수관(Storm sewer)의 불능확률 산정을 위한 신뢰성 모형에 적용하였다. 연 최대강우강도 자료의 분석결과 우리나라 중부지방의 여러 중소도시에 대한 연 최대강우강도의 확률분포함수는 Gumbel분포와 일치하는 것으로 나타났다. 신뢰성 모형은 불능확률의 신뢰함수를 구하기 위해 하중(Load)을 규정하는 식은 합리식이 사용되었고 용량(Capacity)를 규정하는 식은 Darcy-Weisbach공식과 Manning의 공식이 사용되었다. 이렇게 개발된 신뢰성 모형을 실제 우수관에 적용하여 불능확률을 산정하는 신뢰성 해석을 수행하였다. Y자형 우수관망에서 2개의 관으로 유입하는 각각의 유량이 그 관의 허용유량을 초과할 경우를 불능확률로 가정하였고, 나머지 관의 경우는 두 개의 관으로부터 유입하는 유량과 그 세 번째 관의 매설지역의 우수유입량의 합이 그 관의 허용유량을 초과할 경우를 불능상태(state of system failure)로 간주하여 불능확률을 정량적으로 산정하였다. Darcy-Weisbach공식과 Manning의 공식을 사용한 신뢰성 해석결과를 비교하였으며 우수관 직경의 변화에 따른 불능확률을 산정하였다. 특정한 수치(설계직경)이하일 경우 불능확률이 급격히 증가하는 것으로 나타났다. 따라서 실제 우수관의 유효직경이 설계직경에 항상 가깝도록 불순물을 제거하는 것이 최선의 관리 방법이며 불능확률을 줄이는 최선의 방법일 것이다. 본 연구에서 개발된 신뢰성 모형은 우수관의 운용, 관리, 감독은 물론 설계에 활용이 가능 할 것이다.

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Future drought risk assessment under CMIP6 GCMs scenarios

  • Thi, Huong-Nguyen;Kim, Jin-Guk;Fabian, Pamela Sofia;Kang, Dong-Won;Kwon, Hyun-Han
    • Proceedings of the Korea Water Resources Association Conference
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    • 2022.05a
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    • pp.305-305
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    • 2022
  • A better approach for assessing meteorological drought occurrences is increasingly important in mitigating and adapting to the impacts of climate change, as well as strategies for developing early warning systems. The present study defines meteorological droughts as a period with an abnormal precipitation deficit based on monthly precipitation data of 18 gauging stations for the Han River watershed in the past (1974-2015). This study utilizes a Bayesian parameter estimation approach to analyze the effects of climate change on future drought (2025-2065) in the Han River Basin using the Coupled Model Intercomparison Project Phase 6 (CMIP6) with four bias-corrected general circulation models (GCMs) under the Shared Socioeconomic Pathway (SSP)2-4.5 scenario. Given that drought is defined by several dependent variables, the evaluation of this phenomenon should be based on multivariate analysis. Two main characteristics of drought (severity and duration) were extracted from precipitation anomalies in the past and near-future periods using the copula function. Three parameters of the Archimedean family copulas, Frank, Clayton, and Gumbel copula, were selected to fit with drought severity and duration. The results reveal that the lower parts and middle of the Han River basin have faced severe drought conditions in the near future. Also, the bivariate analysis using copula showed that, according to both indicators, the study area would experience droughts with greater severity and duration in the future as compared with the historical period.

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Application of Jackknife Method for Determination of Representative Probability Distribution of Annual Maximum Rainfall (연최대강우량의 대표확률분포형 결정을 위한 Jackknife기법의 적용)

  • Lee, Jae-Joon;Lee, Sang-Won;Kwak, Chang-Jae
    • Journal of Korea Water Resources Association
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    • v.42 no.10
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    • pp.857-866
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    • 2009
  • In this study, basic data is consisted annual maximum rainfall at 56 stations that has the rainfall records more than 30years in Korea. The 14 probability distributions which has been widely used in hydrologic frequency analysis are applied to the basic data. The method of moments, method of maximum likelihood and probability weighted moments method are used to estimate the parameters. And 4-tests (chi-square test, Kolmogorov-Smirnov test, Cramer von Mises test, probability plot correlation coefficient (PPCC) test) are used to determine the goodness of fit of probability distributions. This study emphasizes the necessity for considering the variability of the estimate of T-year event in hydrologic frequency analysis and proposes a framework for evaluating probability distribution models. The variability (or estimation error) of T-year event is used as a criterion for model evaluation as well as three goodness of fit criteria (SLSC, MLL, and AIC) in the framework. The Jackknife method plays a important role in estimating the variability. For the annual maxima of rainfall at 56 stations, the Gumble distribution is regarded as the best one among probability distribution models with two or three parameters.