• 대기
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• 제29권5호
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• pp.615-626
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• 2019

To find any effects of precipitation climate on the forecast verification methods, we processed the hourly records of precipitation over South Korea. We examined their relationship between the climate and the methods of verification. Precipitation is an intermittent process in South Korea, generally less than an hour or so. Percentile ratio of precipitation period against the entire period of the records is only 14% in the hourly amounts of precipitation. The value of the forecast verification indices heavily depends on the climate of rainfall. The direct comparison of the index values might force us to have a mistaken appraisal on the level of the forecast capability of a weather forecast center. The size of the samples for verification is not crucial as long as it is large enough to satisfy statistical stability. Our conclusion is still temporal rather than conclusive. We may need the amount of precipitation per minute for the confirmation of the present results.

• 대기
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• 제33권5호
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• pp.477-492
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• 2023

Based on the new climate normals (1991~2020), annual mean, maximum and minimum temperature is 12.5℃, 18.2℃, and 7.7℃, respectively while annual precipitation is 1,331.7 mm, the annual mean wind speed is 2.0 m s^-1, and the relative humidity is 67.8% in the Republic of Korea. Compared to 1981~2010 normal, annual mean temperature increased by 0.2℃, maximum and minimum temperatures increased by 0.3℃, while the amount of precipitation (0.7%) and relative humidity (1.1%) decreased. There was no distinct change in annual mean wind speed. The spatial range of the annual mean temperature in the new normals is large from 7.1 to 16.9℃. Annual precipitation showed a high regional variability, ranging from 787.3 to 2,030.0 mm. The annual mean relative humidity decreased at most weather stations due to the rise in temperature, and the annual mean wind speed did not show any distinct difference between the new and old normals. With the addition of a warmer decade (2011~2020), temperatures all increased consistently and in particular, the increase in the maximum temperature, which had not significantly changed in previous decades, was evident. The increasing trend of annual and summer precipitation by the 2010s has disappeared in the new normals. Among extreme climate indices, MxT30 (Daily maximum temperature ≥ 33℃ days), MnT25 (Daily minimum temperature ≥ 25℃ days), and PH30 (1 hour maximum precipitation ≥ 30 mm days) increased while MnT-10 (Daily minimum temperature < -10℃ days) and W13.9 (Daily maximum wind speed ≥ 13.9 m/s days) decreased at a statistically significant level. It is thought that a detailed study on the different trends of climate elements and extreme climate indices by region should be conducted in the future.

• 대한토목학회논문집
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• 제39권1호
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• pp.165-174
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• 2019

수문관측자료에서 비정상성(nonstationarity)이 관측됨에 따라 수공구조물 설계에서 비정상성 빈도해석에 대한 연구가 활발히 진행되고 있다. 대기-해양 시스템에 내재된 기후 변동성은 비정상성 현상과 관련이 있는 것으로 알려져 있지만, 비정상성 빈도해석은 일반적으로 선형적 추세를 기반으로 이루어지고 있다. 본 연구에서는 우리나라의 기후 변동성과 극치 강우 사상의 장기 경향성을 고려하기 위하여 기상인자를 활용한 비정상성 빈도해석을 수행하였다. 먼저, 경향성이 나타나는 11개 기상관측지점의 연 최대치 강우자료에 대하여 통계적 분해 방법인 앙상블 경험적 모드분해법을 활용해 자료에 내재된 장기 경향성을 추출하였으며, 계절에 따른 다양한 기상인자와의 상관성 분석을 수행하였다. 그 결과, 연 최대 강우 발생년도를 기준으로 전년도 가을철 AMM과 전년도 가을철 AMO, 그리고 전년도 여름철 NINO4가 10개 이상의 지점에서 연 최대치 강우자료의 장기 경향성에 유의한 영향을 미치는 것으로 나타났다. 선정된 기상인자를 일반 극치(generalized extreme value, GEV) 분포모형에 적용하여 비정상성 GEV (NS-GEV) 모형을 구축하고 기존의 선형적 추세를 고려한 NS-GEV 모형과의 AIC값을 비교하여 최적모형을 선정하였다. 선정된 모형과 기존의 선형적 추세를 고려한 NS-GEV 모형에 대한 성능 평가를 통해 기상인자를 활용한 NS-GEV 모형이 극치강우사상을 반영하여 확률강우량의 과소산정 문제를 보완할 수 있음을 확인하였다.

• 한국기후변화학회지
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• 제5권2호
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• pp.165-178
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• 2014

기후변화에 대처하기 위하여 2015년부터 국가 및 광역지자체와 더불어 기초지자체에서도 기후변화 적응대책을 의무적으로 수립하여야 한다. 적응대책을 수립하기 위해 우선적으로 취약성 분석이 선행되어야 하며, 원활한 취약성 평가를 위하여 취약성 평가도구인 LCCGIS를 개발하고 보급하였다. 현재 대부분의 기초지자체에서는 LCCGIS를 적극적으로 활용하고 있으나, LCCGIS의 경우, 대부분의 평가지표의 값이 동일한 값이 적용되고 있어 평가결과의 편이가 상당하다. 본 연구에서는 LCCGIS를 최대한 활용하여 취약성 평가 결과를 조금이라도 지역적 여건을 반영한 결과로 도출할 수 있는 방법으로, 우선적으로 해당 지자체에서 확보 가능한 읍면동 자료를 확보하고, 다음으로 미확보된 지표에 대해서는 최대한 유사성을 가진 해당기초지자체의 읍면동 자료로 대체하는 방법을 소개하였다. 취약성 평가는 향후 일어날지도 모르는 상황에 어느 정도 취약할 것인지를 평가해야 하기 때문에, 연구자의 주관적인 견해가 포함될 우려가 상당히 많으므로, 최대한 객관적인 지표의 선정, 현재 확보할 수 있는 자료 수준 파악, 지역의 여건에 따라 지표내용의 변경 등에 있어서 객관성을 유지하고자 노력할 필요가 있다. 결론적으로 보완된 취약성평가 결과는 기후변화적응 대책을 수립하기 위한 중요한 선행과제로서, 본 연구의 결과는 향후 LCCGIS를 활용한 취약성 평가 결과의 신뢰성을 제고하고, 지역적 특성이 반영된 기후변화적응 세부시행계획의 수립이 이루어지도록 방향을 설정하는데 기여할 것으로 기대된다.

• 한국기후변화학회지
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• 제8권3호
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• pp.275-285
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• 2017

Chungnam region has established and executed the 2nd Climate Change Adaptation Initiative Execution Plan (2017~2021) based on the Framework Act on Low Carbon, Green Growth. The Execution Plan is established based on the results of climate change vulnerability assessment using the CCGIS, LCCGIS, and VESTAP analysis tools. However, the previously developed climate change vulnerability assessment tools (CCGIS, LCCGIS, VESTAP) cannot reflect the local records and the items and indices of new assessment. Therefore, this study developed a prototype of climate change vulnerability assessment analysis tool that, unlike the previous analysis tools, designs the items and indices considering the local characteristics and allows analysis of grid units. The prototype was used to simulate the vulnerability to forest fires of eight cities and seven towns in Chungcheongnam-do Province in the 2010s, 2020s, and 2050s based on the RCP (Representative Concentration Pathways) 8.5 Scenario provided by the Korea Meteorological Administration. Based on the analysis, Chungcheongnam-do Province's vulnerability to forest fires in the 2010s was highest in Seocheon-gun (0.201), followed by Gyeryong-si (0.173) and Buyeo-gun (0.173) and the future prospects in the 2050s was highest in Seocheon-gun (0.179), followed by Gyeryong-si (0.169) and Buyeo-gun (0.154). The area with highest vulnerability to forest fires in Chungcheongnam-do Province was Biin-myeon, Seocheon-gun and the area may become most vulnerable was Pangyo-myeon, Seocheon-gun. The prototype and the results of analysis may be used to establish the directions and strategies in regards to the vulnerability to wild fires to secure each local government's 2nd execution plan and attainability.

• 한국농공학회논문집
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• 제61권1호
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• pp.31-44
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• 2019

The main objective of this research is to develop a hydroclimate drought index (HCDI) using the gridded climate data inputs in a Variable Infiltration Capacity (VIC) modeling platform. Typical drought indices, including, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Self-calibrated Palmer Drought Severity Index (SC-PDSI) in South Korea are also used and compared. Inverse Distance Weighting (IDW) method is applied to create the gridded climate data from 56 ground weather stations using topographic information between weather stations and the respective grid cell ($12km{\times}12km$). R statistical software packages are used to visualize HCDI in Google Earth. Skill score (SS) are computed to evaluate the drought predictability based on water information derived from the observed reservoir storage and the ground weather stations. The study indicates that the proposed HCDI with the gridded climate data input is promising in the sense that it can help us to predict potential drought extents and to mitigate its impacts in a changing climate. The longer term drought prediction (e.g., 9 and 12 month) capability, in particular, shows higher SS so that it can be used for climate-driven future droughts.

• Ocean Science Journal
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• 제43권1호
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• pp.1-7
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• 2008

Physical and biological environmental variations in the East Sea were investigated by analysing time-series of oceanographic data and meteorological indices. From 1971 to 2000, dominant periodicity in water temperature variations had two apparent periods of 3 to 4 years and of decades, especially in the southwestern part of the East Sea affected by the influence of inflowing Tsushima warm current. Fluctuating water temperature within a certain period appears to respond to El $Ni{\tilde{n}}o$ events with a time lag. It was found that there was a strong correlation between water temperature and El $Ni{\tilde{n}}o$ events with a time lag of 1.5 and 5.5 years for periods of 3 to 6 years and of decades, respectively. Corresponding with El $Ni{\tilde{n}}o$ events, water temperature variability also showed strong correlation with shift and/or changes in biological and chemical environments of nutrient concentrations, zooplankton biomass, and fisheries. However, there also occurred a short-term periodicity of water temperature variations. Within a period of 1 to 4 years, a relatively short-term cycle of water temperature variation had strong correlation with other climate indices such as Pacific Decadal Oscillation and monsoon index. After comparing coherence and phase spectrum between water temperature and different climate indices, we found that there was a shift of coherent periods to another climate index during the years when climate regime shift was reported.

• Ocean Systems Engineering
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• 제7권4호
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• pp.319-328
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• 2017

Higher prediction efficacy is a very challenging task in any field of engineering. Due to global warming, there is a considerable increase in the global sea level. Through this work, an attempt has been made to find the sea level variability due to climate change impact at Haldia Port, India. Different statistical downscaling techniques are available and through this paper authors are intending to compare and illustrate the performances of three regression models. The models: Wavelet Neural Network (WNN), Minimax Probability Machine Regression (MPMR), Feed-Forward Neural Network (FFNN) are used for projecting the sea level variability due to climate change at Haldia Port, India. Model performance indices like PI, RMSE, NSE, MAPE, RSR etc were evaluated to get a clear picture on the model accuracy. All the indices are pointing towards the outperformance of WNN in projecting the sea level variability. The findings suggest a strong recommendation for ensembled models especially wavelet decomposed neural network to improve projecting efficiency in any time series modeling.

• 한국지역지리학회지
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• 제21권2호
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• pp.394-410
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• 2015

본 연구에서는 우리나라의 극한고온, 극한저온, 극한강수의 발생 빈도 및 규모를 지역별로 분석하고 이에 대한 기후지역을 구분하였다. 열대일수는 해안보다 내륙에서 많았고, 서리일수는 고도와 위도의 특성이 잘 나타났다. 호우 일수는 남해안과 제주도에서 많았고, 경상북도 일대에서 적게 나타났다. 이후 주성분 분석과 군집분석을 통해 연구기간의 전 후반기 시기별 변화와 최근 30년 평균(1981~2010년)에 대한 극한기후지수에 대한 기후지역을 구분하였다. 열대일수의 경우 남북 방향으로 구분된 특징을 보였으며, 서리일수는 동해안 및 서해안, 제주도가 하나의 지역으로 구분되었고 호우일수는 경기도 및 강원도 이남 지역에서 동서 방향으로 구분된 특징을 보였다. 본 연구를 통하여 다양한 분야에서 기후변화 적응 및 완화에 대한 대응체계 마련에 도움이 될 것으로 기대된다.

• 한국초지조사료학회지
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• 제44권3호
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• pp.165-172
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• 2024

Italian ryegrass (IRG) has become a vital forage crop due to its increasing cultivation area and its role in enhancing forage self-sufficiency. However, its production is susceptible to environmental factors such as climate change and drought, necessitating precise yield prediction technologies. This study aimed to assess the growth characteristics of IRG and predict dry matter yield (DMY) using vegetation indices derived from unmanned aerial vehicle (UAV)-based remote sensing. The Green Leaf Index (GLI), normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), and optimized soil-adjusted vegetation index (OSAVI) were employed to develop DMY estimation models. Among the indices, GLI demonstrated the highest correlation with DMY (R² = 0.971). The results revealed that GLI-based UAV observations can serve as reliable tools for estimating forage yield under varying environmental conditions. Additionally, post-winter vegetation coverage in the study area was assessed using GLI, and 54% coverage was observed in March 2023. This study assesses that UAV-based remote sensing can provide high-precision predictions of crop yield, thus contributing to the stabilization of forage production under climate variability.