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.