• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2015년도 학술발표회
• /
• pp.226-226
• /
• 2015

The complex climate system regarding human actions is well represented through global climate models (GCMs). The output from GCMs provides useful information about the rate and magnitude of future climate change. Especially, the temperature variable is most reliable among other GCM outputs. However, hydrological variables (e.g. precipitation) from GCM outputs for future climate change contain too high uncertainty to use in practice. Therefore, we propose a method that simulates temperature variable with increasing in a certain level (e.g. 0.5oC or 1.0oC increase) as a global warming scenario from observed data. In addition, a hydrometeorological variable can be simulated employing block-wise sampling technique associated with the temperature simulation. The proposed method was tested for assessing the future change of the seasonal precipitation in South Korea under global warming scenario. The results illustrate that the proposed method is a good alternative to levy the variation of hydrological variables under global warming condition.

• 대기
• /
• 제26권4호
• /
• pp.553-563
• /
• 2016

Land evaporation contribution to precipitation over East Asia is studied to understand terrestrial moisture source of continental precipitation. Moisture recycling of precipitation relying on terrestrial evaporation is estimated based on the analysis method of Van der Ent et al. (2010). We utilize HadGEM2-AO simulations for the period of 1970~1999 and 2070~2099 from RCP8.5. Globally, 46% of terrestrial precipitation is depending from continental evaporation. 58% of terrestrial evaporation returns as continental precipitation. Over East Asia, precipitation has been affected by local evaporation and transported moisture. The advection of upwind continental evaporation results from the prevailing westerlies from the midwestern of Eurasian continent. For the present-day period, about 66% of the precipitation over the land of East Asia originates from land evaporation. Regionally, the ratios change and the ratios of precipitation terrestrial origin over the Northern inland and Southern coast of East Asia are 82% and 48%, respectively. Seasonally, the continental moisture recycling ratio is larger during summer (JJA) than winter (DJF). According to RCP8.5, moisture recycling ratio is expected to change. At the end of the 21st century, the impact of continental moisture sources for precipitation over East Asia is projected to be reduced by about 5% compared to at the end of 20th century. To understand the future changes, moisture residence time change is investigated using depletion and replenishment time.

• 한국기후변화학회지
• /
• 제6권3호
• /
• pp.203-208
• /
• 2015

This study was conducted to provide the agricultural climatological basic data for the reset of sowing period of the winter crop on the double cropping system with rice. During the past 30 years from 1981 to 2010, mean air temperature has risen by $0.45^{\circ}C$ per 10 years (with statistical significance), while precipitation has decreased by 6.74 mm per 10 years and the numbers of days for precipitation has reduced by 0.23 days per 10 years (with no statistical significance) in the sowing period ($1^{st}$ Oct. to $5^{th}$ Nov.) of winter crop. It was analyzed that double cropping system of rice and winter crops need to be reset in the way of delaying the sowing time of winter crops, because rising trend of temperature was clear while variability of precipitation was great and the trend was not clear in the sowing period of winter crops. We have also analyzed the meteorological features of the sowing period of winter crops in 2014, and found that mean air temperature in 2014 was higher than that in normal years (similar to recent temperature change feature) while precipitation in 2014 was much more frequent than that in normal years (unlike recent precipitation features). Such tendency in 2014 made the sowing of winter crops difficult because mechanical sowing could not be worked in flooded paddy fields. Heavy rain in October 2014 was also analyzed as a rare phenomenon.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2009년도 학술발표회 초록집
• /
• pp.112-115
• /
• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as support vector machine neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the monthly precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 2 grid points including $127.5^{\circ}E/35^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, which produced the best results from the previous study. The output node of neural networks models consist of the monthly precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of SVM-NNM and MLP-NNM performances for the downscaling of the monthly precipitation data. We should, therefore, construct the credible monthly precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2009년도 학술발표회 초록집
• /
• pp.125-128
• /
• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the daily precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 4 grid points including $127.5^{\circ}E/37.5^{\circ}N$, $127.5^{\circ}E/35^{\circ}N$, $125^{\circ}E/37.5^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, respectively. The output node of neural networks models consist of the daily precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM performances for the downscaling of the daily precipitation data. We should, therefore, construct the credible daily precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• 한국습지학회지
• /
• 제17권1호
• /
• pp.80-90
• /
• 2015

기후변화로 인해 강우 패턴과 강우강도의 변동성이 커지고 있으며, 도시화 및 산업화에 따른 불투수면적의 증가로 인해, 집중호우에 따른 도시침수와 홍수피해가 심화될 것으로 예상하고 있다. 따라서 본 연구에서는 홍수방어 대안 설정을 위한 설계 강수량(design rainfall) 또는 확률강수량에도 변화가 예상되므로 지역빈도해석을 통해 미래 확률강수량을 산정 및 분석하고자 한다. 기상청 산하 30년 이상의 관측치를 갖고 있는 58개 지점을 대상으로 과거 관측자료를 수집하고, 기후변화를 고려한 미래 확률강수량 추정을 위해 대표농도경로(RCP) 시나리오에 의한 강수량 자료를 이용하여 지역빈도해석을 실시하였다. 기후변화에 따른 강수량 자료의 편의를 제거하기 위하여 분위사상법(Quantile Mapping) 및 이상치 검정을 실시하였다. Hosking and Wallis(1997)가 제시한 L-moment방법을 이용하여 지역빈도 해석을 실시하였으며, 80년, 100년, 200년 빈도에 대한 미래 목표기간별 확률강수량을 산정하였다. 그 결과 21세기 말에 전국의 확률강수량이 현재의 관측 확률강수량에 비해 25 ~ 27% 상승하는 것으로 예측되며, 특히, 제주도 지역이 가장 크게 증가하는 것으로 분석되었다. 따라서 미래 기후변화로 인한 강수량의 증가와 도시화에 따른 유출특성 변화로 자연재해 발생 및 피해는 더욱 증가할 것으로 예상되며, 미래 홍수안전도를 위한 대비책 마련이 필요할 것으로 판단된다.

• Journal of Ecology and Environment
• /
• 제42권2호
• /
• pp.77-84
• /
• 2018

Background: For understanding and evaluating a more realistic and accurate assessment of ecosystem carbon balance related with environmental change or difference, it is necessary to analyze the various interrelationships between soil respiration and environmental factors. However, the soil temperature is mainly used for gap filling and estimation of soil respiration (Rs) under environmental change. Under the fact that changes in precipitation patterns due to climate change are expected, the effects of soil moisture content (SMC) on soil respiration have not been well studied relative to soil temperature. In this study, we attempt to analyze relationship between precipitation and soil respiration in temperate deciduous broad-leaved forest for 2 years in Gwangneung. Results: The average soil temperature (Ts) measured at a depth of 5 cm during the full study period was $12.0^{\circ}C$. The minimum value for monthly Ts was $-0.4^{\circ}C$ in February 2015 and $2.0^{\circ}C$ in January 2016. The maximum monthly Ts was $23.6^{\circ}C$ in August in both years. In 2015, annual precipitation was 823.4 mm and it was 1003.8 mm in 2016. The amount of precipitation increased by 21.9% in 2016 compared to 2015, but in 2015, it rained for 8 days more than in 2016. In 2015, the pattern of low precipitation was continuously shown, and there was a long dry period as well as a period of concentrated precipitation in 2016. 473.7 mm of precipitation, which accounted for about 51.8% of the precipitation during study period, was concentrated during summer (June to August) in 2016. The maximum values of daily Rs in both years were observed on the day when precipitation of 20 mm or more. From this, the maximum Rs value in 2015 was $784.3mg\;CO_2\;m^{-2}\;h^{-1}$ in July when 26.8 mm of daily precipitation was measured. The maximum was $913.6mg\;CO_2\;m^{-2}\;h^{-1}$ in August in 2016, when 23.8 mm of daily precipitation was measured. Rs on a rainy day was 1.5~1.6 times higher than it without precipitation. Consequently, the annual Rs in 2016 was about 12% higher than it was in 2015. It was shown a result of a 14% increase in summer precipitation from 2015. Conclusions: In this study, it was concluded that the precipitation pattern has a great effect on soil respiration. We confirmed that short-term but intense precipitation suppressed soil respiration due to a rapid increase in soil moisture, while sustained and adequate precipitation activated Rs. In especially, it is very important role on Rs in potential activating period such as summer high temperature season. Therefore, the accuracy of the calculated values by functional equation can be improved by considering the precipitation in addition to the soil temperature applied as the main factor for long-term prediction of soil respiration. In addition to this, we believe that the accuracy can be further improved by introducing an estimation equation based on seasonal temperature and soil moisture.

• 미래기술융합논문지
• /
• 제3권2호
• /
• pp.17-23
• /
• 2024

본 논문에서는 기온과 강수량이 농산물 가격에 미치는 영향을 분석하고 TensorFlow를 이용해 주요 농산물 가격을 예측하였다. 분석 결과, 기온 상승과 강수량 증가는 배추, 무, 대파, 상추, 양파 등의 가격 상승에 유의미한 영향을 미쳤다. 특히, 기온과 강수량이 동시에 증가할 때 가격이 급격히 상승하였다. 예측 모델은 기후 변화에 따른 농산물 가격 변동을 사전에 예측하는 데 유용하였다. 이를 통해 농업 생산자와 소비자가 기후 변화에 대비하고, 가격 변동에 대한 대응 전략을 마련할 수 있다. 논문에서는 기후 변화가 농산물 가격에 미치는 영향을 이해하고, 농산물 시장의 안정성과 지속 가능성을 높이는 방안을 모색하는 데 기여할 수 있다. 또한, 기후 변화 시대에 농업의 지속 가능성을 높이고 경제적 안정성을 확보하는 데 중요한 자료를 제공한다. 연구 결과는 정책 결정자들에게도 유용한 통찰을 제공할 것이며, 기후 변화에 대응한 효과적인 농업 정책 수립에 기여할 수 있다.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2015년도 학술발표회
• /
• pp.229-229
• /
• 2015

Downscaling is a fundamental procedure in the assessment of the future climate change impact at regional and watershed scales. Hence, it is important to investigate the spatial variability of the climate conditions that are constructed by various downscaling methods in order to assess whether each method can model the climate conditions at various spatial scales properly. This study introduces a fundamental research from Jang and Kavvas(2015) that precipitation variability from a popular statistical downscaling method (BCSD) and a dynamical downscaling method (MM5) that is based on the NCAR/NCEP reanalysis data for a historical period and on the CCSM3 GCM A1B emission scenario simulations for a projection period, is investigated by means of some spatial characteristics: a) the normalized standard deviation (NSD), and b) the precipitation change over Northern California region. From the results of this study it is found that the BCSD method has limitations in projecting future precipitation values since the BCSD-projected precipitation, being based on the interpolated change factors from GCM projected precipitation, does not consider the interactions between GCM outputs and local geomorphological characteristics such as orographic effects and land use/cover patterns. As such, it is not clear whether the popular BCSD method is suitable for the assessment of the impact of future climate change at regional, watershed and local scales as the future climate will evolve in time and space as a nonlinear system with land-atmosphere feedbacks. However, it is noted that in this study only the BCSD procedure for the statistical downscaling method has been investigated, and the results by other statistical downscaling methods might be different.

• 대기
• /
• 제32권1호
• /
• pp.1-15
• /
• 2022

Recently, Japan's Meteorological Research Institute presented the d4PDF database (Database for Policy Decision-Making for Future Climate Change, d4PDF) through large-scale climate ensemble simulations to overcome uncertainty arising from variability when the general circulation model represents extreme-scale precipitation. In this study, the change of precipitation characteristics between the historical and future climate conditions in the Yongdam-dam basin was analyzed using the d4PDF data. The result shows that annual mean precipitation and seasonal mean precipitation increased by more than 10% in future climate conditions. This study also performed an analysis on the change of the return period rainfall. The annual maximum daily rainfall was extracted for each climatic condition, and the rainfall with each return period was estimated. In this process, we represent the extreme-scale rainfall corresponding to a very long return period without any statistical model and method as the d4PDF provides rainfall data during 3,000 years for historical climate conditions and during 5,400 years for future climate conditions. The rainfall with a 50-year return period under future climate conditions exceeded the rainfall with a 100-year return period under historical climate conditions. Consequently, in future climate conditions, the magnitude of rainfall increased at the same return period and, the return period decreased at the same magnitude of rainfall. In this study, by using the d4PDF data, it was possible to analyze the change in extreme magnitude of rainfall.