• Journal of the Korean earth science society
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• v.45 no.5
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• pp.456-468
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• 2024

River discharge is a crucial indicator of climate change and requires accurate and continuous estimation for effective water resource management and environmental monitoring. This study used satellite gravimetry data to estimate river discharge in major basins with high discharge volumes, specifically the Congo and Orinoco basins. By enhancing the spatial resolution of gravity data through advanced post-processing techniques, including forward modeling and river routing schemes, we effectively detected changes in the water mass stored within river channels. Additionally, signals from surrounding regions were statistically removed using the Empirical Orthogonal Function (EOF) analysis to isolate river-specific discharge signals. These refined signals were then converted into river discharge data through seasonal calibration using the modeled discharge data. Our results demonstrate that this method yields accurate and reliable discharge estimates comparable to in-situ measurements from gauge stations, even without ground-based surveys such as an Acoustic Doppler Current Profiler (ADCP) field campaigns. This research highlights the significant potential of satellite-based gravity data as an alternative to traditional ground surveys, providing practical information on the hydrological status of regions associated with large-scale river systems.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.268-279
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• 2021

The geospatial schemes based on topo-climatology have been developed to produce digital climate maps at a site-specific scale. Their development processes are reviewed here to derive the needs for new schemes in the future. Agricultural and forestry villages in Korea are characterized by complexity and diversity in topography, which results in considerably large spatial variations in weather and climate over a small area. Hence, the data collected at a mesoscale through the Automated Synoptic Observing System (ASOS) operated by the Korea Meteorological Administration (KMA) are of limited use. The geospatial schemes have been developed to estimate climate conditions at a local scale, e.g., 30 m, lowering the barriers to deal with the processes associated with production in agricultural and forestry industries. Rapid enhancement of computing technologies allows for near real-time production of climate information at a high-resolution even in small catchment areas and the application to future climate change scenarios. Recent establishment of the early warning service for agricultural weather disasters can provide growth progress and disaster forecasts for cultivated crops on a farm basis. The early warning system is being expanded worldwide, requiring further advancement in geospatial schemes and digital climate mapping.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.162-168
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• 2007

기후변화는 강수유형, 기온상승과 일사량의 변화로 인한 증발산량의 변화, 유역 식생피복변화로 인한 지표-대기 관계의 변화와 같은 현상을 통해 지역 부존 수자원과 유출량에 큰 변화를 가져올 수 있다. 특히 지표면의 76%를 차지하고 있는 식생피복은 지표와 대기 사이의 물 순환과정에서 중요한 인자이다. 본 연구에서는 넓은 지역에 대한 식생피복의 파악이 용이한 NOAA 위성의 AVHRR (Advanced Very High Resolution Radiometer) 센서로부터 얻을 수 있는 정규화 식생지수 (Normalized Difference Vegetation Index, NDVI)를 통하여 현 식생정보를 정량화하였다. 이로부터 토지피복별 NDVI와 기상인자(기온, 강수량, 일조시간, 풍속, 습도) 사이의 상관관계를 분석하고, 이를 기후변화 시나리오에 의한 기상인자로 부터 토지피복에 따른 미래 NDVI를 추정하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.455-455
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• 2017

최근 기후변화로 인한 도시홍수 피해가 증가하고 있다. 이에 따라 본 연구에서는 기상청에서 제공하는 HadGEM3-RA의 한반도(12.5km) 기후변화 RCP 4.5 및 RCP 8.5시나리오에 대해 편의보정 및 시간상세화를 실시하여 기후변화를 고려한 수문분석을 하였다. 기후변화 시나리오의 편의보정은 Gamma분포를 이용한 모수적 분위사상법과 관측자료의 누가확률분포를 이용하는 비모수적 분위사상법으로 수행하였으며, 관측된 분 단위 강우자료를 기반으로 기후변화 시나리오 미래기간에 대해 시간상세화를 실시하였다. 또한, 도림천유역을 중심으로 기후변화 시나리오 미래기간의 확률강우량과 설계홍수량을 산정하였다. 본 연구에 결과는 수문분석을 위한 기후변화 시나리오 시간상세화 방안에 크게 기여 할 것으로 판단된다.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.156-172
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• 2020

The purpose of this study was to predict road surface temperature using high-resolution solar radiation data. The road surface temperature prediction model (RSTPM) was applied to predict road surface temperature; this model was developed based on the heat-balance method. In addition, using SOLWEIG (SOlar and LongWave Environmental Irradiance Geometry-model), the shadow patterns caused by the terrain effects were analyzed, and high-resolution solar radiation data with 10 m spatial resolution were calculated. To increase the accuracy of the shadow patterns and solar radiation, the day that was modeled had minimal effects from fog, clouds, and precipitation. As a result, shadow areas lasted for a long time at the entrance and exit of a tunnel, and in a high-altitude area. Furthermore, solar radiation clearly decreased in areas affected by shadows, which was reflected in the predicted road surface temperatures. It was confirmed that the road surface temperature should be high at topographically open points and relatively low at higher altitude points. The results of this study could be used to forecast the freezing of sections of road surfaces in winter, and to inform decision making by road managers and drivers.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.571-585
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• 2022

As the threat of natural disasters such as droughts, floods, forest fires, and landslides increases due to climate change, social demand for high-resolution soil moisture retrieval, such as Synthetic Aperture Radar (SAR), is also increasing. However, the domestic environment has a high proportion of mountainous topography, making it challenging to retrieve soil moisture from SAR data. This study evaluated the usability of Sentinel-1 SAR, which is applied with the Artificial Neural Network (ANN) technique, to retrieve soil moisture. It was confirmed that the backscattering coefficient obtained from Sentinel-1 significantly correlated with soil moisture behavior, and the possibility of stand-alone use to correct vegetation effects without using auxiliary data observed from other satellites or observatories. However, there was a large difference in the characteristics of each site and topographic group. In particular, when the model learned on the mountain and at flat land cross-applied, the soil moisture could not be properly simulated. In addition, when the number of learning points was increased to solve this problem, the soil moisture retrieval model was smoothed. As a result, the overall correlation coefficient of all sites improved, but errors at individual sites gradually increased. Therefore, systematic research must be conducted in order to widely apply high-resolution SAR soil moisture data. It is expected that it can be effectively used in various fields if the scope of learning sites and application targets are specifically limited.

• Journal of the Korean Geographical Society
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• v.47 no.2
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• pp.208-225
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• 2012

The spatial characteristics of changes in extreme temperature indices for 2070-2099 relative to 1971-2000 in the Republic of Korea were investigated using daily maximum (Tmax) and minimum (Tmin) temperature data from a regional climate model (HadGEM3-RA) based on the IPCC RCP4.5/8.5 at 12.5km grid spacing and observations. Six temperature-based indices were selected to consider the frequency and intensity of extreme temperature events. For validation during the reference period (1971-2000), the simulated Tmax and Tmin distributions reasonably reproduce annual and seasonal characteristics not only for the relative probability but also the variation range. In the future (2070-2099), the occurrence of summer days (SD) and tropical nights (TR) is projected to be more frequent in the entire region while the occurrence of ice days (ID) and frost days (FD) is likely to decrease. The increase of averaged Tmax above 95th percentile (TX95) and Tmin below 5th percentile (TN5) is also projected. These changes are more pronounced under RCP8.5 scenario than RCP4.5. The changes in extreme temperature indices except for FD show significant correlations with altitude, and the changes in ID, TR, and TN5 also show significant correlations with latitude. The mountainous regions are projected to be more influenced by an increase of low extreme temperature than low altitude while the southern coast is likely to be more influenced by an increase of tropical nights.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.273-286
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• 2021

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen's formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA's) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts' newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA's) meso-scale forecasting data. We analyzed the accuracy of each model's results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.965-967
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• 2003

Information on forest volume, forest coverage and biomass are important for developing global perspectives about CO$_{2}$ concentration changes. Forest biomass cannot be directly measured from space yet, but remotely sensed greenness can be used to estimate biomass on decadal and longer time scales in regions of distinct seasonality, as in the north. Hence, in this research, numerical methods were used to estimate forest biomass in higher northern regions. A regression model linking Normalized Difference Vegetation Index(NDVI), to forest biomass extracted from SPOT/4 VEGETATION data and PAL 8km data in regional and continental area (N40-N70) respectively. Statistical tests indicated that the regression model can be used to represent the changes of forest biomass carbon pools and sinks at high latitude regions over years 1982-2000. This study suggests that the implementation of estimation of biomass based on 8-km resolution NOAA/AVHRR PAL and SPOT-4/VEGETATION data could be detected over a range of land cover change processes of interest for global biomass change studies.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1681-1690
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• 2020

A scanning lidar system has been developed. The system has two wavelength observation channels of 532 and 1064 nm and is capable of 360-degree horizontal scanning observation. In addition, an analysis method that can classify the measured particle as an indicator of coarse-mode particle (PM2.5-10) and an indicator of fine-mode particles (PM2.5) and calculate the mass concentration of each has been developed by using the backscatter coefficient at two wavelengths. It was applied to the data calculated by observation. The mass concentrations of PM10 and PM2.5, which showed a distribution of 22-110 ㎍/㎥ and 7-78 ㎍/㎥, respectively, were successfully calculated in the Ulsan Onsan Industrial Complex using the developed scanning lidar system. The analyzed results showed similar values to the mass concentrations measured on the ground around the lidar observation area, and it was confirmed that high concentrations of 80-110 ㎍/㎥ and 60-78 ㎍/㎥ were measured at points discharged from factories, respectively.