Computational EcoHydrology Lab 

Hello! I'm Dongkook

I'm an Assistant Professor at Keimyung University.

Investigations to improve understanding of ecohydrological and biogeochemical dynamics through hybrid parallel simulations and experimental techniques.


Dong Kook Woo

Assistant Professor

Department of Civil Engineering

Keimyung University, Daegu, South Korea

Faculty Affiliate

Climate & Ecosystem Sciences Division

Lawrence Berkeley National Laboratory, CA, USA

Research Area

Computational Ecohydrology and Biogeochemistry


+82) 053-580-5296



Engineering Building #2111

1095 Dalgubeoldaero, Dalseo-gu, Daegu, 42601, South Korea


Research Assistant


Assistant Professor

Keimyung University

  • Develop numerical representations of complex inter-related terrestrial ecosystem processes.


Faculty Affiliate


Postdoctoral Scholar

Lawrence Berkeley National Laboratory

  • Develop numerical methods for land surface modeling and microbial, abiotic, and plant processes and interactions


Postdoctoral Research Associate

University of Illinois at Urbana-Champaign

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


Research Assistant

University of Illinois at Urbana-Champaign

  • Develop a hybrid CPU-GPU parallel modeling system that links distributed physically-based integrated surface and subsurface flow processes.

  • Contribute to an interdisciplinary research project, Intensively Managed Landscape - Critical Zone Observatory.


Ph.D. in Civil and Environmental Eng

with Computer Science and Engineering Conc

University of Illinois at Urbana-Champaign

Dissertation title: Dynamics of soil-inorganic nitrogen age under hydrological and micro-topographic variability.

Advisor: Dr. Praveen Kumar


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


B.S. in Civil and Environmental Eng

Dankook University


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, Accepted.

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.

* Corresponding author

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Detecting subsurface drainage pipes using a fully convolutional network with optical images

  • Subsurface drainage pipes are identified based on a fully convolutional network

  • Higher albedo directly above drainage pipes than elsewhere is used for detections

  • The proposed approach achieves robust drain pipe detections than conventional methods

Won Seok Do

Undergraduate Research Assistant

Ji Hyun Jo

Undergraduate Research Assistant

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