• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.145-145
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• 2018

Small islands rely heavily on groundwater resources in addition to rainwater as the source of freshwater since surface water bodies are often absent. The groundwater resources are vulnerable to sea level rise, coastal flooding, saltwater intrusion, irregular pattern of precipitation resulting in long droughts and flash floods. Increase in population increases the demand for the limited groundwater resources, thus aggravating the problem. In this study, the effects of climate change on Tongatapu Island, Kingdom of Tonga, a small island in Pacific Ocean, are investigated using a sharp interface transient groundwater flow model. Twenty nine downscaled General Circulation Model(GCM) predictions are input to a water balance model to estimate the groundwater recharge. The temporal variation in recharge is predicted over the period of 2010 to 2099. A set of GCM models are selected to represent the ensemble of 29 models based on cumulative recharge at the end of the century. This set of GCM model predictions are then used to simulate a total of six climate scenarios, three each (2010-2039, 2040-2069, and 2070-2099) under RCP 4.5 and RCP 8.5. The impacts of predicted climate change on groundwater resources is evaluated in terms of freshwater volume changes and saltwater ratios in pumping wells compared to present conditions. Though the cumulative recharge at the end of the century indicates a wetter climate compared to the present conditions the large variability in rainfall pattern results in frequent periods of groundwater drought leading to saltwater intrusion in pumping wells. Thus for sustaining the limited groundwater resources in small islands, implementation of timely assessment and management practices are of utmost importance.

• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.237-248
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• 2018

The vertical distribution of hydrometeor before precipitation near the cloud base has been analyzed using a scanning lidar, rawinsonde data, and Cloud-Resolving Storm Simulator (CReSS). This study mostly focuses on 13 Desember 2016 only. The typical synoptic pattern of lake-effect snowstorm induced easterly in the Yeongdong region. Clouds generated due to high temperature difference between 850 hPa and sea surface (SST) penentrated in the Yeongdong region along with northerly and northeasterly, which eventually resulted precipitation. The cloud base height before the precipitation changed from 750 m to 1,280 m, which was in agreement with that from ceilometer at Sokcho. However, ceilometer tended to detect the cloud base 50 m ~ 100 m below strong signal of lidar backscattering coefficient. As a result, the depolarization ratio increased vertically while the backscattering coefficient decreased about 1,010 m~1,200 m above the ground. Lidar signal might be interpreted to be attenuated with the penetration depth of the cloud layer with of nonspherical hydrometeor (snow, ice cloud). An increase in backscattering signal and a decrease in depolarization ratio occured in the layer of 800 to 1,010 m, probably being associated with an increase in non-spherical particles. There seemed to be a shallow liquid layer with a low depolarization ratio (<0.1) in the layer of 850~900 m. As the altitude increases in the 680 m~850 m, the backscattering coefficient and depolarization ratio increase at the same time. In this range of height, the maximum value (0.6) is displayed. Such a result can be inferred that the nonspherical hydrometeor are distributed by a low density. At this time, the depolarization ratio and the backscattering coefficient did not increase under observed melting layer of 680 m. The lidar has a disadvantage that it is difficult for its beam to penetrate deep into clouds due to attenuation problem. However it is promising to distinguish hydrometeor morphology by utilizing the depolarization ratio and the backscattering coefficient, since its vertical high resolution (2.5 m) enable us to analyze detailed cloud microphysics. It would contribute to understanding cloud microphysics of cold clouds and snowfall when remote sensings including lidar, radar, and in-situ measurements could be timely utilized altogether.

• Journal of the Korean earth science society
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• v.40 no.6
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• pp.599-605
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• 2019

Rice yield (kg 10a^-1) in South Korea was estimated by meteorological variables that are influential factors in crop growth. This study investigated the possibility of anticipating the rice yield variability using a simple but an efficient statistical method, a multiple linear regression analysis, on the basis of the annual variation of meteorological variables. Due to heterogeneous environmental conditions by region, the yearly rice yield was assessed and validated for each province in South Korea. The monthly mean meteorological data for the period 1986-2018 (33 years) from 61 weather stations provided by Korean Meteorological Administration was used as the independent variable in the regression analysis. An 11-fold (leave-three-out) cross-validation was performed to check the accuracy of this method estimating rice yield at each province. This result demonstrated that temporal variation of rice yield by province in South Korea can be properly estimated using such concise procedure in terms of correlation coefficient (0.7, not significant). Furthermore, the estimated rice yield well captured spatial features of observation with mean bias of 0.7 kg 10a^-1 (0.15%). This method may offer useful information on rice yield by province in advance as long as accurate agro-meteorological forecasts are timely obtained from climate models.