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Hydrology & Water Resources Laboratory

Dwindling water resources, increasing susceptibility to hydrologic, hydrometeorological and hydroclimatological extremes, and climate change and variability demand more accurate and reliable water information and put increasingly higher premium on actionable predictive water information. HWRL focuses on integrative hydrologic prediction and water resources information research for sustainable and resilient management and planning of water resources and hazards.

Recent Publications (2013~)

Zhang, Y., D.-J. Seo, and E. Habib, 2014. A Dissection of Differences in Scale-dependent,Climatological Variation of Mean Areal Precipitationbased on a Satellite and Radar-Gauge Observations. submitted to Journal of Hydrology.

Lee, H., and D.-J. Seo, 2014. Assimilation of Hydrologic and Hydrometeorological Data into Operational Distributed Hydrologic Models: Effect of Adjusting Radar-based Gridded Precipitation via Mean Field Bias, accepted for publication in Advances in Water Resources.

Kim, S., D.-J. Seo, H. Riazi and C. Shin, Improving water quality forecasting via data assimilation – Application of maximum likelihood ensemble filter to HSPF, submitted to Journal of Hydrology.

Brown, J. D., M. He, S. Regonda, L. Wu, H. Lee and D.-J. Seo Verification of temperature, precipitation, 1 and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 1. Experimental design and forcing verification, accepted for publication in Journal of Hydrology.

Brown, J. D., M. He, S. Regonda, L. Wu, H. Lee and D.-J. Seo, Verification of temperature, precipitation and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 2. Streamflow verification, accepted for publication Journal of Hydrology.

Seo, D.-J., R. Siddique, Y. Zhang and D. Kim, 2014. Improving Real-Time Estimation of Heavy-to-Extreme Precipitation Using Rain Gauge Data via Conditional Bias-Penalized Optimal Estimation,  Submitted to Journal of Hydrology.

Rafieeinasab, A., D.-J. Seo, H. Lee and S. Kim, 2014. Comparative evaluation of maximum likelihood ensemble filter and ensemble Kalman filter for real-time assimilation of streamflow data into operational hydrologic models, in press, Journal of Hydrology, DOI: 10.1016/j.jhydrol.2014.06.052.

Seo, D., Siddique, R., and Ahnert, P. (). "Objective Reduction of Rain Gauge Network via Geostatistical Analysis of Uncertainty in Radar-Gauge Precipitation Estimation." J. Hydrol. Eng. , 10.1061/(ASCE)HE.1943-5584.0000969 , 04014050.

Lee, H., Y. Zhang, D.-J. Seo, R. Kuligowski, D. Kitzmiller, and R. Corby, Utility of SCaMPR Satellite versus Ground-based Quantitative Precipitation Estimates in Operational Flood Forecasting - the Effects of TRMM Data Ingest, Journal of Hydrometeorology 2014; e-View doi: http://dx.doi.org/10.1175/JHM-D-12-0151.1

Demargne, J., L. Wu, S. Regonda, J. Brown, H. Lee, M. He, D.-J. Seo, R. Hartman, H. Herr, M. Fresch, J. Schaake, and Y. Zhu, 2014. The Science of NOAA’s Operational Hydrologic Ensemble Forecast Service, Bulletin of the American Meteorological Society, doi: 10.1175/BAMS-D-12-00081.1.

Regonda, S., D.-J. Seo and B. Lawrence, 2013. Short-term Ensemble Streamflow Forecasting Using Operationally-Produced Single-valued Streamflow Forecasts - A Hydrologic Model Output Statistics (HMOS) Approach, Journal of Hydrology, 497(8), 80-96.

Zhang, Yu, Dong-Jun Seo, David Kitzmiller, Haksu Lee, Robert J. Kuligowski, Dongsoo Kim, Chandra R. Kondragunta, 2013: Comparative Strengths of SCaMPR Satellite QPEs with and without TRMM Ingest versus Gridded Gauge-Only Analyses. J. Hydrometeor, 14, 153–170.

Seo D-J. 2013. Conditional bias-penalized kriging. Stochastic Environmental Research and Risk Assessment. 27:43-58.

Recent Presentations (2013~)

Seo et al. 2014. Data assimilation in ensemble water forecasting – Challenges and opportunities (invited). 10th Anniversary HEPEX Workshop, College Park, MD, June 24-26.

Seo, D.-J., R. Siddique, Y. Zhang and  D. Kim, 2014. Improving Real-Time Estimation of Heavy-to-Extreme Precipitation Using Rain Gauge Data via Conditional Bias-Penalized Optimal Estimation. NWS/OHD Seminar, Silver Spring, MD, May 22.

Rafieei Nasab, A., A. Norouzi, D.-J. Seo, S. Kim, H. Chen, V. Chandrasekar, B. Cosgrove, A. Cannon, 2014. High-resolution flash flood forecasting for large urban areas – Sensitivity to scale of precipitation input and model resolution, International Symposium on Weather Radar and Hydrology, Reston, VA.

Rafieei Nasab, A., A. Norouzi, T. Mathew, D.-J. Seo, H. Chen, V. Chandrasekar, P. Rees, B. Nelson, 2014. Comparative evaluation of multiple radar-based QPEs for North Texas, International Symposium on Weather Radar and Hydrology, Reston, VA.

Nazari, B., D.-J. Seo, R. Muttiah, C. Davis, 2014. Hydraulic modeling for inundation mapping using radarrainfall data - A case study for the City of Fort Worth, WRaH 2014, Reston, VA.

Seo, D.-J., M. Saharia, R. Corby, F. Bell and J. Brown, 2013. Increasing lead time in short-range ensemble streamflow forecasting via the Hydrologic Ensemble Forecast Service (HEFS), AGU Annual Meeting, San Francisco (Invited).

Seo, D.-J., S. Kim, H. Riazi and C. Shin, 2013. Improving Water Quality Forecasting via Real-Time Data Assimilation, EPA Region 6 Water Quality Modeling Conference & Workshop, Dallas, TX (Invited).

Seo, D.-J., A. Rafieeinasab, B. Nazari, A. Norouzi, V.  Chandrasekar, H. Chen, S. Kim, J. Gao, P. Jangyodsuk, and C.  Davis, 2013. High-resolution  flash flood forecasting for large urban areas. International Workshop on Rain Radar and its Hydrologic Application, Korea Institute of Construction Technology, Goyang, Korea (Invited).

Saharia, M., D.-J. Seo, R. Corby and F. Bell, 2013. Increasing lead time in short-range streamflow forecasting via the Hydrologic Ensemble Forecast Service (HEFS). NWS/OHD Seminar, Silver Spring, MD.

Rafieeinasab,  A.,  A. Norouzi, H. Chen, D.-J. Seo, V. Chandrasekar and A.  Cannon, 2013. High-resolution flash flood forecasting for the City of Fort Worth. NWS/OHD Seminar, Silver Spring, MD.

Chandrasekar, V.,  H. Chen, D.-J. Seo, 2013. Impacts of Polarimetric CASA Radar Observations on a Distributed Hydrologic Model, EGU General Assembly, Vienna, Austria.

Chandrasekar, V., H. Chen, B. Philips, D.-J. Seo, F. Junyent, A. Bajaj, M. Zink, J. McEnery, Z. Sukheswalla, A. Cannon, E. Lyons, D. Westbrook, 2013. The CASA Dallas Fort Worth Remote Sensing Network ICT for Urban Disaster Mitigation, EGU General Assembly, Vienna, Austria.

Saharia, M., D.-J. Seo, R. Corby, K. He, 2013.  Short-Range Ensemble Streamflow Forecasting for Upper Trinity River, AGU Meeting of the Americas, Cancun, Mexico.

S. Kim, D.-J. Seo, H. Riazi, and C. Shin, 2013. Improving water quality forecasting with HSPF via ensemble data assimilation. AGU Meeting of the Americas, Cancun, Mexico.

Siddique, R, D.-J. Seo, Y. Zhang, D. Kim, 2013. Improving analysis of heavy to extreme precipitation with conditional bias-penalized optimal estimation, 27th Conference on Hydrology, AMS Annual Meeting, Cancun,  Mexico.

Rafieeinasab, A., D.-J. Seo, R. Corby, P. McKee,  2013. Evaluation of the NWS Distributed Hydrologic Model over the Trinity River Basin in Texas, 27th Conference on Hydrology, AMS  Annual Meeting, Austin, TX.