Earticle

최근 장기적인 기후 변동성을 고려하기 위하여 대기-해양 순환 패턴을 수치화한 기상인자가 수문 변수 예측에 널리 사용되 고 있다. 또한 정확하고 안정적인 예측을 위해 인공신경망 기반의 예측 모형이 꾸준히 발전하고 있다. 기상인자를 활용하여 기후 변동성을 고려한 수문량 예측은 수자원 및 환경 보존의 장기적인 관리에 효율적으로 활용될 수 있으므로 수문 변수에 유의한 인자의 파악과 이를 활용한 예측 모형의 적용은 꾸준한 도전이 될 것이다. 본 연구에서는 우리나라 한강 유역 댐 유 입량에 통계적으로 유의한 상관성이 있는 대표 기상인자를 선정하고, 이를 인공신경망 앙상블 모형에 적용하여 댐 유입량 예 측을 수행하였다. 이를 위해 앙상블 경험적 모드분해법을 활용하여 댐 유입량과 기상인자간의 통계적 상관성을 확인하였으 며, 기존 단일 인공신경망 모형의 한계를 보완한 인공신경망 앙상블 모형을 구축하였다. 예측 수행 결과, 5개 댐 상관계수 평균이 훈련 기간에서 0.88, 검증 기간에서 0.68의 예측력을 보이는 것을 확인하였으며, 본 연구에서의 절차를 토대로 우리 나라의 다양한 수문 변수와 기후 변동성간의 관계를 활용한 다양한 적용 사례가 나오길 기대한다.

Recently, climate indices represented by quantifying atmospheric-ocean circulation patterns have been widely used to predict hydrologic variables for considering long-term climate variability. Hydrologic forecasting models based on artificial neural networks have been developed to provide accurate and stable forecasting performance. Forecasts of hydrologic variables considering climate variability can be effectively used for long-term management of water resources and environmental preservation. Therefore, identifying significant indicators for hydrologic variables and applying forecasting models still remains as a challenge. In this study, we selected representative climate indices that have significant relationships with dam inflow time series in the Han-River basin, South Korea for applying the dam inflow forecasting model. For this purpose, the ensemble empirical mode decomposition(EEMD) method was used to identify a significance between dam inflow and climate indices and an artificial neural network(ANN) ensemble model was applied to overcome the limitation of a single ANN model. As a result, the forecasting performances showed that the mean correlation coefficient of the five dams in the training period is 0.88, and the test period is 0.68. It can be expected to come out various applications using the relationship between hydrologic variables and climate variability in South Korea.

전통적 수문빈도분석의 기본가정은 기후와 수문사상이 정상성이라는 것으로 즉, 분포형의 매개변수들이 시간에 따라 불변이라는 것이다. 댐, 제방, 운하, 교량 등 수공 관련 기간시설물을 계획하고 설계할 때는 과거 상황을 이해하고 미래에도 그 상황이 유지될 것이라는 것을 근거로 한다. 그러나 현실은 기본가정과는 달리 수문자료들은 비정상성을 지니고 있으며 수자원관리자들에 의해 항상 기간시설물을 계획하고 설계 할 때 비정상성을 다루고자 끊임없이 노력해 왔다. 본 논문에서는 비정상성 수문빈도분석기법을 소개하고, 조건부 Generalized Extreme Value(GEV) 분포를 이용하여 비정상성 빈도분석을 실시하였다. 본 논문에서는 6개 기상관측소지점의 24시간 연최고치 강우량을 대상으로 비정상성 빈도분석을 실시하였으며 최우도법(Maximum Likelihood)을 사용하여 GEV 분포형의 매개변수를 추정하였다. 그 결과 비정상성 GEV 분포가 확률 강우량을 산정하는데 있어 적합함을 확인 할 수 있었다. 또한 ENSO(El Nino Southern Oscillation)를 나타내는 지수인 SOI(Southern Oscillation Index)를 이용하여 기후변동 고려한 비정상성 빈도분석을 실시하였다.

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).

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 특성인 자를 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.

This study developed the Climate Variability Index (CVI) to assess the water resources through adding detail indicators into the existing regional Water Poverty Index (WPI) to consider climate variability and flood damage. This study aims at selecting indicators of WPI focused on water availability and regional climate variability, assessing regional variability of the indices during 1998-2007, and providing information to help determining the priority of water sector policies, investment, and applications. The WPI represents the relationship between the level of welfare and the water use. Considered with flood management and climate variability, CVI added by regional characteristics may be used in water resources management as well as flood mitigation for coping with climate change.

Understanding in climate effects on marine ecosystem is essential to utilize, predict, and conserve marine living resources in the 21st century. In this review paper, we summarized the past history and current status of Korean fisheries as well as the changes in climate and oceanographic phenomena since the 1960s. Ocean ecosystems in Korean waters can be divided into three, based on the marine commercial fish catches; the demersal ecosystem in the Yellow Sea and the East China Sea, the pelagic ecosystem in the Tsushima Warm Current from the East China Sea to the East/Japan Sea, and the demersal ecosystem in the northern part of the East/Japan Sea. Through the interdisciplinary retrospective analysis using available fisheries, oceanographic, and meteorological information in three important fish communities, the trend patterns in major commercial catches and the relationship between climate/environmental variability and responses of fish populations were identified. Much evidence revealed that marine ecosystems, including the fish community in Korean waters, has been seriously affected by oceanographic changes, and each species has responded differently. In general, species diversity is lessening, and mean trophic level of each ecosystem has decreased during the last $3\sim4$ decades. Future changes in fisheries due to global warming are also considered for major fisheries and aquaculture in Korean waters.

There have been many suggestions and much debate about climate variability during the Holocene. However, their complex forcing factors and mechanisms have not yet been clearly identified. In this paper, we have examined the Holocene climate cycles and features based on the wavelet analyses of $^{14}C$, $^{10}Be$, and $^{18}O$ records. The wavelet results of the $^{14}C$ and $^{10}Be$ data show that the cycles of ~2180-2310, ~970, ~500-520, ~350-360, and ~210-220 years are dominant, and the ~1720 and ~1500 year cycles are relatively weak and subdominant. In particular, the ~2180-2310 year periodicity corresponding to the Hallstatt cycle is constantly significant throughout the Holocene, while the ~970 year cycle corresponding to the Eddy cycle is mainly prominent in the early half of the Holocene. In addition, distinctive signals of the ~210-220 year period corresponding to the de Vries cycle appear recurrently in the wavelet distribution of $^{14}C$ and $^{10}Be$, which coincide with the grand solar minima periods. These de Vries cycle events occurred every ~2270 years on average, implying a connection with the Hallstatt cycle. In contrast, the wavelet results of $^{18}O$ data show that the cycles of ~1900-2000, ~900-1000, and ~550-560 years are dominant, while the ~2750 and ~2500 year cycles are subdominant. The periods of ~2750, ~2500, and ~1900 years being derived from the $^{18}O$ records of NGRIP, GRIP and GISP2 ice cores, respectively, are rather longer or shorter than the Hallstatt cycle derived from the $^{14}C$ and $^{10}Be$ records. The records of these three sites all show the ~900-1000 year periodicity corresponding to the Eddy cycle in the early half of the Holocene.

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.

In this study the GFDL GCM generated (controlled run) zonal average temperature data are evaluated by comparing their EOFs with those from observed data. Even though the correlation matrices of observed and simulated data are shown significantly different (Polyak and North, 1997b), the EOFs derived are found very similar with very high pattern correlations. This means almost all the information (second-order statistics) derived from the observed data can be reproduced by the EOFs derived from the GFDL GCM simulates. Also, the EOFs from GFDL GCM were found to have more flexible structures than those from the observed. Thus, we may conclude that the GFDL GCM can simulate the Earth's energy balance system reasonably. However, more in detail research should be focused on the effect from various forcings on climate variability, as, in some cases, the effect of external forcings could shadow the system characteristics and mislead the simulation results.

Objectives This study was conducted to describe the regional malaria incidence in relation to the geographic and climatic conditions and describe the effect of altitude on the expansion of malaria over the last decade in Papua New Guinea. Methods Malaria incidence was estimated in five provinces from 1996 to 2008 using national health surveillance data. Time trend of malaria incidence was compared with rainfall and minimum/maximum temperature. In the Eastern Highland Province, time trend of malaria incidence over the study period was stratified by altitude. Spatio-temporal pattern of malaria was analyzed. Results Nationwide, malaria incidence was stationary. Regionally, the incidence increased markedly in the highland region (292.0/100000/yr, p =0.021), and remained stationary in the other regions. Seasonality of the malaria incidence was related with rainfall. Decreasing incidence of malaria was associated with decreasing rainfall in the southern coastal region, whereas it was not evident in the northern coastal region. In the Eastern Highland Province, malaria incidence increased in areas below 1700 m, with the rate of increase being steeper at higher altitudes. Conclusions Increasing trend of malaria incidence was prominent in the highland region of Papua New Guinea, while long-term trend was dependent upon baseline level of rainfall in coastal regions.

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.

Long term streamflow regime under virtual climate change scenario was examined. Rainfall forecast simulation of the Canadian Global Coupled Model (CGCM2) of the Canadian Climate Center for modeling and analysis for the IPCC SRES B2 scenario was used for analysis. The B2 scenario envisions slower population growth (10.4 billion by 2010) with a more rapidly evolving economy and more emphasis on environmental protection. The relatively large scale of GCM hinders the accurate computation of the important streamflow characteristics such as the peak flow rate and lag time, etc. The GCM rainfall with more than 100km scale was downscaled to 2km-scale using the space-time stochastic random cascade model. The HEC-HMS was used for distributed hydrologic model which can take the grid rainfall as input data. The result illustrates that the annual variation of the total runoff and the peak flow can be much greater than rainfall variation, which means actual impact of rainfall variation for the available water resources can be much greater than the extent of the rainfall variation.

Ozone is one of the most significant atmospheric constituents controlling the intensity of solar UV-B irradiance (280 to 320nm), and the decrease of the total ozone amount supported by ozonesondes and spectrometers will result in the increase of UV-B irradiance at the earth's surface. For example, 1% decrease in stratospheric ozone is expected to yield a 2-3% increase in UV-B irradiance and in the incidence of skin cancer. (omitted)

Changes in the earth's climate depend on changes in the net sunlight reaching us. The net depends on the sun's output and earth's reflectance, or albedo. Here we develop the limits on the changes in the sun's output in historical times based on the physics of the origin of solar cycle changes. Many have suggested that the sun's output could have been $0.5\%$ less during the Maunder minimum, whereas the variation over the solar cycle is only about $0.1\%$. The frequencies of solar oscillations (f- and p-modes) evolve through the solar cycle, and provide the most exact measure of the cycle-dependent changes in the sun. But precisely what are they probing? The changes in the sun's output, structure and oscillation frequencies are driven by some combination of changes in the magnetic field, thermal structure and velocity field. It has been unclear what is the precise combination of the three. One way or another, this thorny issue rests on an understanding of the response of the solar structure to increased magnetic field, but this is complicated. Thus, we do not understand the origin of the sun's irradiance increase with increasing magnetic activity. Until recently, it seemed that an unphysically large magnetic field change was required to account for the frequency evolution during the cycle. However, the problem seems to have been solved (Dziembowski, Goode & Schou 2001) using f-mode data on size variations of the sun. From this and the work of Dziembowski & Goode (2003), we suggest that in historical times the sun couldn't be much dimmer than it is at activity minimum.

Previous studies have indicated a great regional difference in Sea Level Rise (SLR) in the Pacific and it has been suggested that this is linked to climate variability over the past two decades. In this study, we seek to identify the possible linkage between regional sea level and Pacific climate variability from altimetry-based sea level data (1993-2012) and further investigate how the Pacific sea level has changed spatially and temporally over the past 60 years from long-term sea level reconstruction data (1953-2008). Based on the same method as Zhang and Church (2012), the Inter-annual Climate Index (ICI) associated with the El $Ni{\tilde{n}}o-Southern$ Oscillation (ENSO) and the Decadal Climate Index (DCI) associated with Pacific Decadal Oscillation (PDO) are defined and then the multiple variable linear regression is used to analyze quantitatively the impact of inter-annual and decadal climate variability on the regional sea levels in the Pacific. During the altimeter period, the ICI that represents ENSO influence on inter-annual time scales strongly impacts in a striking east-west "see-saw mode" on sea levels across the tropical Pacific. On the other hand, the decadal sea level pattern that is linked to the DCI has a broad meridional structure that is roughly symmetric in the equator with its North Pacific expression being similar to the PDO, which largely contributes to a positive SLR trend in the western Pacific and a negative trend in the eastern Pacific over the two most recent decades. Using long-term sea level reconstruction data, we found that the Pacific sea levels have fluctuated in the past over inter-annual and decadal time scales and that strong regional differences are presented. Of particular interest is that the SLR reveals a decadal shift and presents an opposite trend before and after the mid-1980s; i.e., a declining (rising) trend in the western (eastern) Pacific before the mid-1980s, followed by a rising (declining) trend from the mid-1980s onward in the western (eastern) Pacific. This result indicates that the recent SLR patterns revealed from the altimeters have been persistent at least since the mid-1980s.

미래에 대한 기후는 과거와 비교하여 변동성이 더 크고 불확실성 또한 더 크기 때문에 미래의 기후변화를 예측하기 위해서는 기후변화의 절대적인 크기뿐 아니라 불확실한 정도도 함께 고려되어야 한다. 본 연구에서는 CMIP6(Coupled Model Intercomparison Project Phase 6) DB에서 제공된 일 단위 18개의 GCMs(General Circulation Models)의 결과를 분석하였으며 또한 3개의SSP(Shared Socioeconomic Pathway)시나리오와 3개의 미래 구간에 대하여 100개의 앙상블을 각각 생성하였다. 불확실성을 초래하는 원인을 3가지로 구분하고, 각각의 원인에 대한 불확실성의 정도를 앙상블 시나리오에 반영하고자 한다. 현재 기간 및 미래 기간에 대해 100개의 20년 시계열 날씨변수 앙상블을 생성하여 LSTM(Long short-term memory)의 입력자료로 사용하여 댐유입량, 저수위, 방류량을 산정하였다. 댐 유입량 및 방류량의 예측성능을 향상시키기 위해 Input predictor의 종류를 선정하는 방법과 그 변수들의 lag time을 결정하는 방법, 입력자료들을 재구성하는 방법, 하이퍼 매개변수를 효율적으로 최적화하는 방법, 목적함수 설정 방법들을 제시하여 댐 유입량 및 방류량의 예측을 크게 향상시키고자 하였다. 본 연구에서 예측된 미래의 댐유입량 및 방류량 정보는 홍수 또는 가뭄 등 다양한 수자원 관련 문제의 전략을 수립하는 데 있어서 적절한 도움이 될 것이다.

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.