• Journal of the Korean Geographical Society
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• v.39 no.5 s.104
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• pp.711-721
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• 2004

The aim of this study is to clarify whether frequency and/or severity of extreme climate events have changed significantly in Korea during recent years. Using the best available daily data, spatial and temporal aspects of ten climate change indicators are investigated on an annual and seasonal basis for the periods of 1954-1999. A systematic increase in the $90^{th}$ percentile of daily minimum temperatures at most of the analyzed areas has been observed. This increase is accompanied by a similar reduction in the number of frost days and a significant lengthening of the thermal growing season. Although the intra-annual extreme temperature range is based on only two observations, it provides a very robust and significant measure of declining extreme temperature variability. The five precipitation-related indicators show no distinct changing patterns for spatial and temporal distribution except for the regional series of maximum consecutive dry days. Interestingly, the regional series of consecutive dry days have increased significantly while the daily rainfall intensity index and the fraction of annual total precipitation due to events exceeding the $95^{th}$ percentile for 1901-1990 normals have insignificantly increased.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.458-458
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• 2012

Climate extreme variability is a major cause of disaster such as flood and drought types occurred in Korea and its effects is also more severe damage in last decades which can be danger mature events in the future. The main aim of this study was to assess the effectives of climate change on drought for an agriculture as Nakdong basin in Korea using climate change data in the future from data of General Circulation Models (GCM) of ECHO-G, with the developing countries like Korea, the developed climate scenario of medium-high greenhouse gas emission was proposed of the SRES A2. The Standardized Precipitation Index (SPI) was applied for drought evaluation. The drought index (SPI) applied for sites in catchment and it is evaluated accordingly by current and future precipitation data, specific as determined for data from nine precipitation stations with data covering the period 1980-2009 for current and three periods 2010-2039, 2040-2069 and 2070-2099 for future; time scales of 3month were used for evaluating. The results determined drought duration, magnitude and spatial extent. The drought in catchment act intensively occurred in March, April, May and November and months of drought extreme often appeared annual in May and November; drought frequent is a non-uniform cyclic pattern in an irregular repetitive manner, but results showed drought intensity increasing in future periods. The results indicated also spatial point of view, the SPI analysis showed two of drought extents; local drought acting on one or more one of sites and entire drought as cover all of site in catchment. In addition, the meteorology drought simulation maps of spatial drought representation were carried out with GIS software to generate for some drought extreme years in study area. The method applied in this study are expected to be appropriately applicable to the evaluation of the effects of extreme hydrologic events, the results also provide useful for the drought warning and sustainable water resources management strategies and policy in agriculture basins.

• Journal of Korea Water Resources Association
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• v.43 no.12
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• pp.1083-1091
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• 2010

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. However, widely used estimation schemes fail to describe the realistic variability of daily precipitation field. We compare and contrast the performance of statistical methods for the spatial estimation of precipitation in two hydrologically different basins, and propose a two-step process for effective daily precipitation estimation. The methods assessed are: (1) Inverse Distance Weighted Average (IDW); (2) Multiple Linear Regression (MLR); (3) Climatological MLR; and (4) Locally Weighted Polynomial Regression (LWP). In the suggested simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before applying IDW scheme (one of the local scheme) to estimate the amount of precipitation separately on wet days. As the results, the suggested method shows the better performance of daily rainfall interpolation which has spatial differences compared with conventional methods. And this technique can be used for streamflow forecasting and downscaling of atmospheric circulation model effectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.3
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• pp.315-328
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• 2024

Precipitation is a crucial meteorological variable widely used as essential input data in most hydrological models. However, due to climate change, there is an escalating precipitation variability. Trend analysis plays an important role in planning and operating water resources systems. As recently developed, Innovative Polygon Trend Analysis (IPTA) is useful in identifying and and analyzing the trends of hydrologic variables. In this study, the IPTA was applied to monthly precipitation data obtained from 13 meteorological observatories in Jeolla province, along with synthesized precipitation data according to Shared Socioeconomic Pathways (SSP) scenarios. The trend results were compared those obtained from the Mann-Kendall test and the Sen's slope estimation which are generally used in practice. The results revealed monthly precipitations from February to July and November had increasing trends, and monthly precipitation in October had a decreasing trend. IPTA, Mann-Kendall test, and Sen's slope estimation detected trends in 75.00 %, 5.13 %, and 5.13 % of 156(13 stations × 12 months) time series of monthly precipitation, respectively, which indicates that the IPTA is more sensitive in trend detection compared to the Mann-Kendall test and Sen's slope estimation.

• Journal of the Korean earth science society
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• v.38 no.3
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• pp.182-193
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• 2017

A typical snowfall pattern occurs over the east coastal region of the Korean Peninsula, known as the Yeongdong region. The precipitation over the Yeongdong region is influenced by the cold and dry northeasterly wind which advects over warm and moist sea surface of the East Sea of Korea. This study reveals the influence of large-scale factors, affecting local to remote areas, on the mesoscale snowfall system over the Yeongdong region. The National Centers for Environmental Prediction-Department of Energy reanalysis dataset, Extended Reconstructed sea surface temperature, and observed snowfall data are analyzed to reveal the relationship between February snowfall and large-scale factors from 1981 to 2014. The Yeongdong snowfall is associated with the sea level pressure patterns over the Gaema Plateau and North Pacific near the Bering Sea, which is remotely associated to the sea surface temperature (SST) variability over the North Pacific. It is presented that the relationship between the Yeongdong snowfall and large-scale factors is strengthened after 1999 when the central north Pacific has warm anomalous SST. These enhanced relationships explain the atmospheric patterns of recent strong snowfall years (2010, 2011, and 2014). It is suggested that the newly defined index in this study based on related SST variability can be used for a seasonal predictor of the Yeongdong snowfall with 2-month leading.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.256-261
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• 2004

Due to the problems of global warming, the frequency of meteorological extremes such as droughts, floods and the annual rainfall amount are suddenly increasing. Even though the increase of greenhouse gases, for example, is thought to be the main factor for global warming, its impact on global climate has not yet been revealed clearly in rather quantitative manners. Therefore, tile objective of this study is to inquire the change of precipitation condition due to climate change by global warming. In brief, this study want to see its assumption if rainfall quantile estimates are really changing. In order to analyze the temporal change, the rainfall quantile estimates at the Seoul rain gauge stations are estimated for the 21-year data period being moved from 1908 to 2002 with 1-year lag. The main objective of this study is to analyze the variability of rainfall quantile estimates using four methods. Next, The changes in confidence interval of rainfall quantile are evaluated by increasing the data period. It has been found that confidence interval of rainfall quantile estimates is reduced as the data period increases. When the hydraulic structures are to be designed, it is important to select the data size and to re-estimate the flood prevention capacity in existing river systems.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1619-1624
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• 2007

The rainfall frequency estimations are critical in the design of hydraulic structures (such as bridges and culverts) to ensure that they are built economically and safely. In other words, they are not over designed or under designed. However one of the main assumptions in the creation of these analysis is that the rainfall data for a site is stationary. That is, climatic trends and variability in a region have negligible effects on the curves. But as has been proved in recent history, climatic variability and trends do exist and their effects on precipitation have not been negligible. Increasing occurrences of the El Nino phenomenon have lead to droughts and floods around the world, and long term trends in rainfall, both increases and decreases, have been seen in all regions across Korea. The purpose of this paper is to investigate and evaluate impacts of ENSO on rainfall characteries and rainfall frequency estimations in Korea. In this paper, The available rainfall data were categorized into Warm(EL Nino), Cold(La Nina), Normal episodes based on the Cold & Warm Episodes by Season then 50 years of daily rainfall data were generated for each episodic events(EL Nino, La Nina)

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.471-479
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• 2024

Flood damage is becoming more serious due to the heavy rainfall caused by climate change. Physically based hydrological models have been utilized to predict stream water level variability and provide flood forecasting. Recently, hydrological simulations using machine learning and deep learning algorithms based on nonlinear relationships between hydrological data have been getting attention. In this study, the Long Short-Term Memory (LSTM) algorithm is used to predict the water level of the Seomjin River watershed. In addition, Climate Prediction Center morphing method (CMORPH)-based gridded precipitation data is applied as input data for the algorithm to overcome for the limitations of ground data. The water level prediction results of the LSTM algorithm coupling with the CMORPH data showed that the mean CC was 0.98, RMSE was 0.07 m, and NSE was 0.97. It is expected that deep learning and remote data can be used together to overcome for the shortcomings of ground observation data and to obtain reliable prediction results.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.685-689
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• 2002

This study focused the relationship between the Normalized Difference Vegetation Index (NDVI) and the evapotranspiration (ET) temporal changes. Especially, the interannual change of the NDVI and ET from 1982 to 2000 at regional to continental scales was highlighted mainly over Asia. Monthly global NDVI data were acquired from Pathfinder AVHRR Land (PAL) data (1$\times$1 degree resolution). The monthly ET was estimated from assimilated atmospheric data provided from National Centers for Environmental Prediction (NCEP) (2.5$\times$2.5 degree resolution), and gridded global precipitation data of CPC Merged Analysis of Precipitation (CMAP) (2.5$\times$2.5 degree resolution). Significant positive correlations were found between the NDVI and ET interannual changes in May and June over western Siberia. Moreover, it was revealed that the most of area in Asia has positive correlation coefficient in May and June. These results delineate that the vegetation activity significantly contributes to the ET interannual change over extensive areas.

• The Korean Journal of Quaternary Research
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• v.19 no.2
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• pp.55-58
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• 2005

A 120-cm core recovered from Lake Hovsgol, the northern Mongolia provides evidence for climate variability since the Marine Isotope Stage 3, representing a sharp lithological change. The lowermost part of the core consists of diatom-barren calcareous silty clay without coarse sands, framboidal pyrite, and biogenic components deposited during the MIS 3. Following the last glacial maximum, in-situ moss is included in the sediments, as lake-level was retreated by cold and dry environment with low precipitation. The AMS radiocarbon ages of the plant fragments match a marked lithologic boundary between 14,060 and 14,325 $^{14}C$ yr BP. The contents of coarse sands abruptly increase, indicating probably wind-derived sandy dust or coarse grains contributed from floating icebergs. And abundant framboidal pyrite grains were deposited in an anoxic environment, as reflected by high accumulation of organic matters at a low lake stand. During the deglaciation, quantities of coarse sands, ostracod, shell fragments, framboidal pyrite, and diatom markedly varies by regional and global scale climate regimes. Some allochthonous coarse sands were probably ice-rafted debris derived from floating icebergs. A rapid increase in diatom productivity probably marked the onset of Bolling-Allerod warming. Subsequent high concentration of framboidal pyrite probably represents a dry and cold condition, such as Younger Drays events. Consistent warm period with high precipitation at Holocene is documented by diatomaceous clayey ooze without framboidal pyrite, coarse sands, and ostracod.