Computational EcoHydrology Lab

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Undergraduate Researchers & M.S./Ph.D. Positions Available | 학부연구생 및 석·박사과정 학생 모집

We are recruiting motivated undergraduate researchers, M.S. and Ph.D. students interested in climate change, ecohydrology, hydrologic and environmental modeling, AI/machine learning, water–carbon–vegetation interactions, and nature-based solutions.

Computational EcoHydrology Lab에서 함께 연구할 학부연구생 및 석·박사과정 학생을 모집합니다.
기후변화, 생태수문학, 수문·환경 모델링, AI·머신러닝, 물–탄소–식생 상호작용 및 자연기반해법(NbS) 연구에 관심 있는 학생들의 지원을 환영합니다.

Contact: dkwoo@sejong.ac.kr

Dong Kook Woo

Assosiate Professor

Department of Civil and Environmental Engineering

Sejong University, Seoul, South Korea

Editorial roles

2026.06.-present, Scientific Reports, Editorial Board Member

2025.01.-present, Korea Water Resources Association, Managing Editor

Research Area

Computational Ecohydrology and Biogeochemistry

Phone:

+82) 02-3408-3726

Email:

dkwoo@sejong.ac.kr

Address:

Yulgok-gwan #701A

209, Neungdong-ro, Gwangjin-gu, Seoul, South Korea, 05006

EXPERIENCE

2011-2017

Research Assistant

2026-Present

Associate Professor

Sejong University

Investigating water–carbon–vegetation interactions under climate change and natural and anthropogenic disturbances by integrating field observations, process-based modeling, and artificial intelligence to support sustainable water and ecosystem management

2020-2026

Assistant Professor

Keimyung University

Developing numerical representations of complex inter-related terrestrial ecosystem processes.

2020-2025

Faculty Affiliate

2018-2020

Postdoctoral Scholar

Lawrence Berkeley National Laboratory

Link: https://profiles.lbl.gov/37312-dong-kook-woo
Developing numerical methods for land surface modeling and microbial, abiotic, and plant processes and interactions

2017-2018

Postdoctoral Research Associate

University of Illinois at Urbana-Champaign

Exploring the impacts of weather variability on the spatial and temporal distribution of water and nutrient age.

2011-2017

Research Assistant

University of Illinois at Urbana-Champaign

Developing a hybrid CPU-GPU parallel modeling system that links distributed physically-based integrated surface and subsurface flow processes.
Contributing to an interdisciplinary research project, Intensively Managed Landscape - Critical Zone Observatory.

EDUCATION

2013-2017

Ph.D. in Civil and Environmental Eng

with Computer Science and Engineering Conc

University of Illinois at Urbana-Champaign

Dissertation title: Dynamics of inorganic soil-nitrogen age in intensively managed landscape

Advisor: Dr. Praveen Kumar

2011-2013

M.S. in Civil and Environmental Eng

University of Illinois at Urbana-Champaign

Dissertation title: Soil carbon and nitrogen cycle modeling for bioenergy crops

Advisor: Dr. Praveen Kumar

2003-2011

B.S. in Civil and Environmental Eng

Dankook University

PUBLICATIONS

35. Woo, D.K. (2026). Long-term climate change alters carbon cycling and hydrologic dynamics in a Korean pine forest, Journal of Hydrology: Regional Studies, Accepted.

34. Jo, J., M. Yeon, D.K. Woo* (2026). Climate-driven hydrologic change and soil organic carbon dynamics in a temperate floodplain, Journal of Environmental Management, Accepted.

33. Lee, E., D.K. Woo* (2026). Hydrological impacts on the discrepancy between apparent potential evaporation and pan evaporation, Journal of Hydrology, 667, 134876, 10.1016/j.jhydrol.2025.134876

32. Woo, D.K. (2026). Estimating actual evapotranspiration from widely available meteorological data with a hybrid CNN-LSTM, Agricultural Water Management, 323, 110078. 10.1016/j.agwat.2025.110078

31. Woo, D.K. (2025). Unraveling the potential of willow afforestation for long-term soil organic carbon sequestration under future climate and acclimation pathways, Forest Ecology and Management, 593, 122843. 10.1016/j.foreco.2025.122843

30. Hong, J., J. Park, S. Hong, D.K. Woo*, H Choi* (2025). Development of non-invasive measurement techniques to ascertain the shrinkage limit of silty clay through contactless ultrasonic system. Measurement, 117720, 10.1016/j.measurement.2025.117720

29. Song, H., D.K. Woo* (2025). Soil Moisture Estimation in the Unsaturated Zone Using Surface Wave Measurements and Hybrid Modeling Framework, Measurement Science and Technology, 36, 016101, 10.1088/1361-6501/ad8257

28. Seo, Y., J. Hwang, D.K. Woo* (2024). Assessment of drainage network configuration using Gibbs' model with increased number of flow directions, Water, 16, 3442, 10.3390/w16233442

27. Woo, D.K. (2024). Enhanced crop yields as a potential mitigator of soil organic carbon losses under elevated temperature in erosional and depositional landscape, Science of the Total Environment, 951, 175400, 10.1016/j.scitotenv.2024.175400

26. Noh S.J. G. Lee, B. Kim, S. Lee, J. Jo, D.K. Woo* (2024). Climate change impact assessment on water resources management using a combined multi-model approach in South Korea, Journal of Hydrology: Regional Studies, 53, 101842, 10.1016/j.ejrh.2024.101842

25. Woo, D.K. (2024). Potential reduction in carbon fixation capacity under climate change in a Pinus koraiensis forest, Forest Ecosystems, 11, 100183 10.1016/j.fecs.2024.100183

24. Lee, E., Y. Seo, D.K. Woo* (2024). Enhanced environmental and economic benefits of green roofs in a humid subtropical region under future climate, Ecological Engineering, 201, 107221. 10.1016/j.ecoleng.2024.107221

23. Lee, S., D.K. Woo*, H. Choi (2023). Contactless estimation of soil moisture using leaky Rayleigh waves and a fully convolutional network, Vadose Zone Journal, e20285, 10.1002/vzj2.20285

22. Woo, D.K., J. Ji, H. Song (2023). Subsurface drainage pipe detection using an ensemble learning approach and aerial images. Agricultural Water Management, 287, 108455, 10.1016/j.agwat.2023.108455

21. Seo, Y., Y. Kwon, J.S. Hwang, D.K. Woo*. (2023). A Comparative Experimental Study of Green Roofs based on Radiation Budget and

Surface Energy Balance. KSCE Journal of Civil Engineering. 10.1007/s12205-023-1491-0

20. Woo, D.K., Y. Seo, (2022). Effects of elevated temperature and abnormal precipitation on soil carbon and nitrogen dynamics in a Pinus densiflora forest. Frontiers in Forests and Global Change, 5:1051210. 10.3389/ffgc.2022.1051210

19. Woo, D.K.*, W.J. Riley, R.F. Grant, Y. Wu. (2022). Site-specific field management adaptation is key to feeding the world in the 21st century. Agricultural and Forest Meteorology. 327, 109230. 10.1016/j.agrformet.2022.109230

18. Woo, D.K., W. Do, J. Hong, H. Choi. (2022). A novel and non-invasive approach to evaluate soil moisture without soil disturbances: Contactless ultrasonic system. Sensors. 22, 7450

17. Roque-Malo, S., Q. Yan, D.K. Woo, J.L. Druhan, P. Kumar. (2022). Advances in Biogeochemical Modeling for Intensively Managed Landscapes. Biogeochemistry of the Critical Zone. 10.1007/978-3-030-95921-0_6

16. Cain, M., D.K. Woo, P. Kumar, L. Keefer, and A. Ward. (2022). Antecedent conditions control thresholds of tile-runoff generation and nitrogen export in intensively managed landscapes. Water Resour. Res. 10.1029/2021WR030507

15. Woo, D.K.*, W, Do, (2021). The role of forest in climate change for carbon, nitrogen, and water: A case study of Pinus densiflora, Water, 13, 3050.

14. Woo, D.K.*, W.J. Riley, A. Paez-Garcia, A. R. Marklein, Z. A. Mekonnen, X. Liu, X. Ma, E. Blancaor, Y. Wu, (2021). Impoverishing roots will improve wheat yield and profitability through increased water and nitrogen use efficiencies, JGR-Biogeosciences, 126, e2020JG005829

13. Nandan, R. D.K. Woo, P. Kumar, and J. Adinarayana. (2021). Impact of irrigation scheduling methods on corn yield under climate change, Agricultural Water Management, 255, 106990

12. Song, H, D.K. Woo*, and Q. Yan. (2021). Detecting subsurface drainage pipes using a fully convolutional network with optical images, Agricultural Water Management, 249, 106791 

11. Roque-Malo, S., D.K. Woo, and P. Kumar. (2020). Modeling the role of root exudation in critical zone nutrient dynamics, Water Resour. Res., e2019WR026606

10. Woo, D.K.*, W.J. Riley, and Y. Wu. (2020). More fertilizer and impoverished root required for improving wheat yields and profits under climate change, Field Crops Research, 249, 107756.

9. Kratt, C.B., D.K. Woo, K.N. Johnson, M. Haagsma, P. Kumar, J. Selker, and S. Tyler. (2020). Field trials to detect drainage pipe networks using thermal and RGB data from unmanned aircraft. Agricultural Water Management, 229, 105895

8. Woo, D.K., H. Song, and P. Kumar. (2019). Mapping subsurface tile drainage systems with thermal images. Agricultural Water Management, 218, 94–101

7. Woo, D.K., and P. Kumar. (2019). Impacts of subsurface tile drainage on age–concentration dynamics of inorganic nitrogen in soil. Water Resour. Res., 55, 1470–1489

6. Yan, Q., P.V.V. Le, D.K. Woo, T. Hou, T. Filley and P. Kumar. (2019). 3-D modeling of the coevolution of landscape and soil organic carbons. Water Resour. Res., 55, 1218–1241

5. Wilson, C., L. Keefer, B. Abban, K. Wacha, D. Dermisis, C. Giannopoulos, S. Zhou, A. Goodwell, D.K. Woo, et al. (2018). The Intensively Managed Landscape Critical Zone Observatory: A Scientific testbed for agroecosystem functions and services. Vadose Zone Journal. 17, 180088.

4. Woo, D.K., and P. Kumar. (2017). Role of micro-topographic variability on the distribution of inorganic soil-nitrogen age in Intensively Managed Landscape. Water Resour. Res., 53, 8404-8422.

3. Dutta, D., K. Wang, E. Lee, A. Goodwell, D.K. Woo, D. Wagner, and P. Kumar. (2016). Characterizing vegetation canopy structure using airborne remote sensing data. Geoscience and Remote Sensing, IEEE Transaction on. 99, 1-19.

2. Woo, D.K., and P. Kumar. (2016). Mean age distribution of inorganic soil-nitrogen. Water Resour. Res, 48, 12090-12098.

1. Woo, D.K., J.C. Quijano, P. Kumar, S. Chaoka, and C.J. Bernacchi. (2014). Threshold dynamics in soil carbon storage for bioenergy crops. Environmental Science & Technology, 48, 12090-12098.

Domestic Journal

7. Lee, J., D.K. Woo, G. Lee, M. Yeon (2026). Long-term runoff simulation using a physically based distributed model: case studies of the Cheoncheon basin and the Andong-dam basin. J. Korea Water Resour. Assoc. Accepted.

6. Yeon, M., G. Lee, D.K. Woo*. (2025). Development and application of the soil organic carbon model considering physical hydrological processes. J. Korea Water Resour. Assoc. 58, 553-567. 10.3741/JKWRA.2025.58.7.553

5. Lee, G., D.K. Woo, S.J. Noh. (2024). Comparative assessment of sequential data assimilation-based streamflow predictions using semi-distributed and lumped GR4J hydrologic models: a case study of Namgang Dam basin. J. Korea Water Resour. Assoc. 57, 585-598. 10.3741/JKWRA.2024.57.9.585

4. Jo, J., D.K. Woo*. (2024). Assessment of water supply reliability under climate change scenarios. J. Korea Water Resour. Assoc. 57, 409-419. 10.3741/JKWRA.2024.57.6.409

3. Noh, S.J., G. Lee, B. Lee, J. Jo, D.K. Woo*. (2023). Climate change impact analysis on water supply reliability and flood risk using combined rainfall-runoff and reservoir operation modeling: Hapcheon-Dam catchment case. J. Korea Water Resour. Assoc. 56, 765-774. 10.3741/JKWRA.2023.56.11.765

2. Woo, D.K., J. Jo, B. Kang, S. Lee, G. Lee, S.J. Noh. (2023). Assessing the sensitivity of runoff projections under precipitation and temperature variability using lumped runoff-rainfall models: IHACRES and GR4J. KSCE Journal of Civil and Environmental Engineering Research. 43, 43-54. 10.12652/Ksce.2023.43.1.0043

1. Lee, G., S. Lee, B. Kim, D.K. Woo, S.J. Noh. (2022). Comparative assessment and uncertainty analysis of ensemble-based hydrologic data assimilation using airGRdatassim. J. Korea Water Resour. Assoc. 31,761-774

* Corresponding author

RECENT RESEARCH

Estimating actual evapotranspiration from widely available meteorological data with a hybrid CNN–LSTM

Direct estimation of ETa is accurate and generalizable. A hybrid CNN–LSTM model driven only by routinely measured meteorolog-ical variables and simple soil/vegetation descriptors reproduces daily ETa at flux-tower sites with low errors and transfers well in time when applied with ERA5 forcings.

ETo is informative but not a surrogate. Treating ETo as a stand-alone proxy for ETa leads to mismatches; in our framework ETo adds only modest marginal value once its constituent drivers (radiation, temperature/humidity, wind) and soil moisture are included. Using ETo strictly as a diagnostic predictor avoids con-flating atmospheric demand with realized flux.

The ML-derived ETa fields provide a useful, climate-dependent global benchmark. When driven by ERA5, the model reproduces large-scale patterns and daily variability with good performance, especially in snow-influenced continental mid- to high-latitude climates, while revealing weaker performance in arid, tropical, and polar regions where additional data and further model re-finement are needed.