• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.204-204
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• 2021

Terrestrial water storage (TWS) includes all components of water (e.g., surface water, groundwater, snow and ice) over the land. So accurately predicting and estimating TWS is important in water resource management. Although many land surface models are used to predict the TWS, model output has errors and biases in comparison to the observation data due to the model deficiencies in the model structure, atmospheric forcing datasets, and parameters. In this study, Gravity Recovery And Climate Experiment (GRACE) satelite TWS data is assimilated in the Community Land Model version 5 with a biogeochemistry module (CLM5.0-BGC) over East Asia from 2003 to 2010 by employing the Ensemble Adjustment Kalman Filter (EAKF). Results showed that TWS over East Asia continued to decrease during the study period, and the ability to simulate the surface water storage, which is the component of the CLM derived TWS, was greatly improved. We further investigated the impact of assimilated TWS on the vegetated and carbon related variables, including the leaf area index and primary products of ecosystem. We also evaluated the simulated total ecosystem carbon and calculated its correlation with TWS. This study shows that how the better simulated TWS plays a role in capturing not only water but also carbon fluxes and states.

• 한국지구과학회지
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• 제42권4호
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• pp.445-458
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• 2021

Gravity Recovery and Climate Experiment (GRACE) gravimeter satellites observed the Earth gravity field with unprecedented accuracy since 2002. After the termination of GRACE mission, GRACE Follow-on (GFO) satellites successively observe global gravity field, but there is missing period between GRACE and GFO about one year. Many previous studies estimated terrestrial water storage (TWS) changes using hydrological models, vertical displacements from global navigation satellite system observations, altimetry, and satellite laser ranging for a continuity of GRACE and GFO data. Recently, in order to predict TWS changes, various machine learning methods are developed such as artificial neural network and multi-linear regression. Previous studies used hydrological and climate data simultaneously as input data of the learning process. Further, they excluded linear trends in input data and GRACE/GFO data because the trend components obtained from GRACE/GFO data were assumed to be the same for other periods. However, hydrological models include high uncertainties, and observational period of GRACE/GFO is not long enough to estimate reliable TWS trends. In this study, we used convolutional neural networks (CNN) method incorporating only climate data set (temperature, evaporation, and precipitation) to predict TWS variations in the missing period of GRACE/GFO. We also make CNN model learn the linear trend of GRACE/GFO data. In most river basins considered in this study, our CNN model successfully predicts seasonal and long-term variations of TWS change.

• 한국측량학회지
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• 제27권3호
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• pp.375-384
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• 2009

2002년 3월에 발사되어 현재까지 임무 수행중인 GRACE위성의 관측자료는 극지방 빙하의 융해, 빙하 지각균형 조정, 해수면 변화, 하천유역의 저수량변화, 대규모 지진 등 지구시스템의 질량재분배에 대한 연구에 활발히 이용되고 있다. 본 연구에서는 GRACE위성의 Level-2 월별 중력장 모델을 이용하여 2002년 8월부터 2009년 1월 사이 양자강유역의 육지저수량 변화를 분석하였으며, 특히는 2003년, 2006년 및 2008년에 수행된 삼협댐의 3단계 저수과정에 의한 삼협댐 지역에서의 중력변화를 등가수분두께로 계산하여 살펴보았다. 연구결과, 연구기간 내의 양자강유역은 뚜렷한 연변화와 계절변화를 나타내었으며, 연변화의 진폭은 2.3cm로 계산되었다. 또한, 실제 수자원통계자료 및 수문관측자료와의 비교를 통하여 GRACE위성자료 이용 가능성을 확인하였으며, 이는 향후 위성중력자료를 이용하여 하천유역 내에서의 물의 이동 및 주기적인 변화 연구에 큰 도움이 될 것으로 판단된다.