• East Asian Economic Review
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• 제27권4호
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• pp.273-301
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• 2023

This study investigates the impacts of long-term climate variability on household consumption in Indonesia, a country highly vulnerable to climate change. The analysis combines household survey data from nearly 5,998 families with satellite-derived weather data from NASA POWER spanning 30 years. We use the long-term variability in temperature and precipitation as a proxy for climate change. This study examines the impact of climate change which proceeds over the long term, unlike previous studies concerning one-off or short-term climate events. In addition, using satellite data enhances the accuracy of households' exposure to climate variability. The analysis finds that households in a village with higher temperature and precipitation variability significantly consume less food. This implies that households more exposed to climate change are at higher risk of malnutrition in developing countries. This study has a limitation that it cannot rule out the potential endogeneity of choosing a climate-vulnerable residential location due to economic poorness.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2008년도 학술발표회 논문집
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• pp.67-74
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• 2008

The regional-scale transient groundwater-river interaction model is developed to gain a better understanding of the regional-scale relationships and interactions between groundwater and river system and quantify the residual river flow after groundwater abstraction from the aquifers with climate variability in the Waimea Plains, New Zealand. The effect of groundwater abstraction and climate variability on river flows is evaluated by calculating river flows at the downstream area for three different drought years (a 1 in 10 drought year, 1 in 20 drought year, and 1 in 24 drought year) and an average year with metered water abstraction data. The effect of future water demand (50 year projection) on river flows is also evaluated. A significant increase in the occurrence of zero flow, or very low flow of 100 L/sec at the downstream area is predicted due to large groundwater abstraction increase with climate variability. Modeling results shows the necessity of establishing dynamic cutback scenarios of water usage to users over the period of drought conditions considering different climate variability from current allocation limit to reduce the occurrence of low flow conditions at the downstream area.

• 한국습지학회지
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• 제13권3호
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• pp.427-441
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• 2011

본 연구는 수자원 상황을 평가하기 위하여 기존에 개발된 물 빈곤지수에 지역적 기후변동성 및 홍수피해를 평가할 수 있는 세부지표를 추가한 기후 변동성지수를 개발하여 국내에 적용하였다. 물이용 평가에 초점이 맞추어진 물 빈곤지수 세부지표를 선정하고 지역적 특성에 따른 치수 및 기후변동성 내용이 추가된 지역별 특성인자를 선정하여 1998년부터 2007년까지 물 빈곤지수와 기후 변동성지수에 대한 분석을 실시하여 지역별 변동성을 평가하고 물 부문 정책, 투자 및 적용에 대한 우선순위를 결정하는데 도움을 줄 수 있는 정보를 제공하고자 하였다. 물 빈곤지수는 복지 수준과 물이용간의 관련성을 나타낼 수 있으며 치수와 기후변동성을 함께 고려하여 지역별 특성인자를 추가한 기후 변동성지수는 물이용에 영향을 미치는 인자와 치수 및 기후변화를 함께 고려할 수 있으므로, 지역별로 기후변화에 대응하는 물이용뿐만 아니라 홍수관리에도 사용할 수 있을 것이다.

• 한국수자원학회논문집
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• 제57권4호
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• pp.237-248
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• 2024

수문 순환의 중요한 구성 요소인 유역 유출량은 기후변동과 인간 활동의 영향으로 전 세계 많은 유역에서 크게 변화되고 있다. 기후변동과 인간 활동으로 변화되고 있는 유역 유출량에 대한 분석은 수자원 관리에 있어서 중요하다. 본 연구에서는 우리나라 5개 권역의 109개 중권역의 유출량 자료를 대상으로 기후변동과 인간 활동이 유역 유출량에 미치는 영향을 정량적으로 평가하였다. 유역 유출량 자료에 대하여 Pettitt 검정을 수행하여 분석 기간을 나누었으며, Budyko 기반 기후 탄력성 방법을 이용하여 기후변동과 인간 활동이 유역 유출량의 변화에 미치는 영향을 구분하였다. 본 연구 결과, 중권역마다 유역 유출량 변화에 기후변동과 인간 활동이 미치는 상대적인 기여도가 다양하게 나타났으며, 중권역별로 유역 유출량의 변화에 지배적인 영향을 주는 요인이 무엇인지 파악하였다. 본 연구의 결과는 기후변동과 유역 개발 계획에 따른 유역 유출량 변화를 예측할 수 있도록 하며, 이는 가뭄이나 홍수 등 수문 재해의 위험을 줄이기 위한 수자원 관리 계획을 수립하는데 중요한 정보가 될 것이다.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2000년도 추계 학술대회지
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• pp.12-27
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• 2000

During the recent decades, he problem of climate variability and change has been in the forefront of scientific problems. The objective of this study was to assess the impact of climate variability on crop growth and yield. The growth duration was the main impact of climate variability on crop yield. Phyllochronterval was shortened in the global worming situations. A simple model to describe developmental traits was provided from heading data of directly seeded rice cultivars and temperature data. Daily mean development rate could be explained by the average temperature during the growth stage. Simple regression equation between daily mean development rate(x) and the average temperature(y) during the growth period as y = ax + b. It can be simply modified as x = 1/a $\ast$ (y-b). The parameters of the model could depict the thermo sensitivity of the cultivars. On the base of this model, the three doubled CO2 GCM scenarios were assessed. The average of these would suggest a decline in rice production of about 11% if we maintained the current cultivars. Future cultivar's developmental traits could be suggested by the two model parameters.

• 한국기후변화학회지
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• 제9권2호
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• pp.171-179
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• 2018

The causal relationship between El Nino-Southern Oscillation (ENSO) and winter climate variability in Korea is tested by analyzing Korea Meteorological Administration Automatic Synoptic Observing System datasets for the past 59 years. Consistent with previous studies, positive phase of ENSO (El Nino) tends to cause warmer temperature and heavier precipitation in Korea in early winter with three-week lead time. This causality is quantified by performing Granger causality test. It turns out that ENSO explains an additional 9.25% of the variance of early-winter temperature anomalies in Korea, beyond that already provided by temperature itself. Likewise, 22.18% additional information is gained to explain early-winter precipitation variance by considering ENSO. This result, which differs from simple lead-lag correlation analysis, suggests that ENSO needs to be considered in predicting early-winter surface climate variability in Korea.

• 대기
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• 제26권4호
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• pp.687-696
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• 2016

The western North Pacific subtropical high (WNPSH) in boreal summer has interannual and interdecadal variability, which affects East Asian summer monsoon variability. In particular, it is well known that the intensity of WNPSH is reversely related to that of summer monsoon in North East Asia in association with Pacific Japan (PJ)-like pattern. Many coupled climate models weakly simulate this large-scale teleconnection pattern and also exhibit the diverse variability of WNPSH. This study discusses the inter-model differences of WNPSH simulated by different climate models, which participate in the Coupled Model Intercomparison Project phase 5 (CMIP5). In comparing with reanalysis observation, the 29 CMIP5 models could be assorted into two difference groups in terms of interannual variability of WNPSH. This study also discusses the dynamical or thermodynamics factors for the differences of two groups of the CMIP5 climate models. As results, the regressed precipitation in well-simulating group onto the Nino3.4 index ($5^{\circ}N-5^{\circ}S$, $170^{\circ}W-120^{\circ}W$) is stronger than that in poorly-simulating group. We suggest that this difference of two groups of the CMIP5 climate models would have an effect on simulating the interannual variability of WNPSH.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.537-537
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• 2015

In recent decades, the independence and identical distribution (iid) assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Nino-Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, in the current study we propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observations in increasing order. The combination of two distributions, gamma and generalized extreme value (GEV), was fitted to the GEV distribution in a simulation study. In addition, a case study examining the annual hourly maximum precipitation of all stations in South Korea was performed to evaluate the performance of the proposed approach. The results of the simulation study and case study indicate that the proposed metaheuristic parameter estimation method is an effective alternative for accurately selecting the rth power when the iid assumption of extreme hydrometeorological events is not valid for large-scale climate variability. The maximum likelihood estimate is more accurate with a low mixing probability, and the probability-weighted moment method is a moderately effective option.

• Journal of Astronomy and Space Sciences
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• 제36권3호
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• pp.149-157
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• 2019

In this study, we investigate the associations between the solar variability and teleconnection indices, which influence atmospheric circulation and subsequently, the spatial distribution of the global pressure system. A study of the link between the Sun and a large-scale mode of climate variability, which may indirectly affect the Earth's climate and weather, is crucial because the feedbacks of solar variability to an autogenic or internal process should be considered with due care. We have calculated the normalized cross-correlations of the total sunspot area, the total sunspot number, and the solar North-South asymmetry with teleconnection indices. We have found that the Southern Oscillation Index (SOI) index is anti-correlated with both solar activity and the solar North-South asymmetry, with a ~3-year lag. This finding not only agrees with the fact that El $Ni{\tilde{n}}o$ episodes are likely to occur around the solar maximum, but also explains why tropical cyclones occurring in the solar maximum periods and in El $Ni{\tilde{n}}o$ periods appear similar. Conversely, other teleconnection indices, such as the Arctic Oscillation (AO) index, the Antarctic Oscillation (AAO) index, and the Pacific-North American (PNA) index, are weakly or only slightly correlated with solar activity, which emphasizes that response of terrestrial climate and weather to solar variability are local in space. It is also found that correlations between teleconnection indices and solar activity are as good as correlations resulting from the teleconnection indices themselves.

• 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.