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Research

Overview
Research Interests
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  • Eco-Hydrological Forecasting with Land Surface Models (LSMs) and Machine Learning
  • Land Data Assimilation with the Satellite Remote Sensing
  • Land-Biosphere-Atmosphere Interactions 
  • Droughts and Extreme Hydroclimate Events under Changing Climate 
  • Sustainable Water Resources Management
  • 지면모형 및 기계학습 기반 수문-생태 예측
  • 위성자료 및 모형 융합을 통한 예측
  • 지면-생태-대기 상호작용 규명 및 이해 (물, 에너지, 탄소 순환) 
  • 기후변화와 가뭄 및 극한 수문기후 사상
  • 지속가능 수자원 관리

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Eco-Hydrological Forecasting 
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  • Improving the streamflow forecasting with deep learning in Korea (WRF-Hydro/LSTM) paper
  • Land data assimilation (EnKF) for the hydrologic forecasting in East Asia (NCAR CLM) paper​
  • DGVM for the earth system model (NASA Ent)  paper

Biosphere-Atmosphere Interactions​
​​
  • ​​Changes in phenology and water-carbon fluxes in Alaska (ED2) paper
  • Arctic land-atmosphere interactions (NCAR CESM) paper
  • Impacts of fires and vegetation dynamics in water and carbon fluxes (NCAR CLM-DGVM)  paper
  • Vegetation phenology and land-atmosphere interactions in Amazon (ED2) paper
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Droughts and Extreme Hydrologic Events

  • Understanding future drought risks over the globe paper
  • Revised Drought Severity Index paper
  • Quantifying droughts with SPEI paper
  • Extreme value analysis of precipitation paper

Sustainable Water Res. Management
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  • ​Water resources planning under climate and socio-economic changes in Pakistan (WEAP) paper
  • Water use sustainability in Korea paper
  • Robust adaptation strategies for climate change paper
  • Climate change vulnerability in water resources paper
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Research Projects

Active
  • (Participant) Estimation of carbon budget in wetland ecosystems, Korea Environmental Industry and Technology Institute (KEITI) / Ministry of Environment, 2022–2026 
  • (PI) Fire prediction and impact assessment in Alaska with the integrated AI and process-based model, Korea Polar Research Institute (KOPRI), 2022–2024 
  • (PI) Future change of ecohydrological droughts based on statistical downscaling and multiple land surface models, National Research Foundation of Korea (NRF) – international collaboration with National Natural Science Foundation of China (NSFC), 2021-2023
  • (PI) Improving the ecohydrological drought prediction and predicting drought impacts with using the machine learning and the land surface models over the Korean Peninsula and Asia, National Research Foundation of Korea (NRF), 2020–2024
 
Completed
  • (PI) Assessing the carbon-water-energy impacts of urban green infrastructure to mitigate climate risk for carbon neutral, Korea Agency for Infrastructure Technology Advancement (KAIA) / Ministry of Land, Infrastructure and Transport, 2021–2022
  • (PI) Drought predictions with combining machine learning and process-based model, National Research Foundation of Korea (NRF), 2018–2020
  • (PI) Bottom-up model and data for climate change adaptation and mitigation in water, forest and ecosystem sectors, Korea Environment Institute (KEI) / Korea Environmental Industry and Technology Institute (KEITI) / Ministry of Environment, 2018–2020
  • (PI) Understanding the arctic shrub expansion with the Ecosystem Demography Model, Korea Polar Research Institute (KOPRI), 2017–2019
  • (Co-PI) Earth system modelling for the pan-arctic regions, National Research Foundation of Korea (NRF), 2017–2020
  • (PI) Eco-hydrologic data assimilation and streamflow drought assessment in Korea,National Research Foundation of Korea (NRF), 2015–2018
  • (PI) Eco-hydrologic data-model fusion to understand drought processes in East Asia, Korea Meteorological Institute (KMI) / Korea Meteorology Agency, 2015–2018
  • (PI) Indicators for the sustainable water use, Korea Environment Institute (KEI), 2015–2015
  • (Participant) Climate change adaptation for watershed management, Korea Agency for Infrastructure Technology Advancement (KAIA) / Ministry of Land, Infrastructure and Transport, 2015–2020
​© 2022 Department of Civil and Environmental Engineering | Yonsei Univ | Seoul | Korea