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~)
Habibi, H., Nasab, A.R., Norouzi, A., Nazari, B., Seo, D.-J., Muttiah, R., Davis, C., 2016. High Resolution Flash Flood Forecasting for the Dallas-Fort Worth Metroplex. JWMM. doi:10.14796/JWMM.C401
Kim, B., Seo, D.-J., Noh, S.J., Prat, O.P., Nelson, B.R., 2016. Improving Multisensor Estimation of Heavy-to-Extreme Precipitation via Conditional Bias-Penalized Optimal Estimation. J. Hydrol. in press.
Lee, H., Seo, D.-J., Noh, S. J., 2016. A weakly-constrained data assimilation approach to address rainfall-runoff model structural inadequacy in streamflow prediction. J. Hydrol. in press.
Nelson, B.R., Prat, O.P., Seo, D.-J., Habib, E., 2016. Assessment and Implications of NCEP Stage IV Quantitative Precipitation Estimates for Product Intercomparisons. Wea. Forecasting 31, 371–394.
Noh, S. J., Lee, S., An, H., Kawaike, K., Nakagawa, H. 2016. Ensemble urban flood simulation in comparison with laboratory-scale experiments: Impact of interaction models for manhole, sewer pipe, and surface flow. Adv. Water Resour. 97, 25-37.
Norouzi, A., Habibi, H., Nazari, B., Noh, S.J., Seo, D.-J., Zhang, Y., 2016. On Scale-Dependent Sensitivity of Frequency of Mean Areal Runoff in Urban Areas to Precipitation, Imperviousness and Soil, and Their Variations. J. Hydrol. in review.
Seo, D.-J., Saifuddin, M., 2016. Conditional bias-penalized Kalman filter for improved estimation and prediction of extremes. Stochastic Environmental Research and Risk Assessment, in review.
Zhang, Y., Reed, S., Gourley, J.J., Cosgrove, B., Kitzmiller, D., Seo, D.-J., Cifelli, R., 2016. The impacts of climatological adjustment of quantitative precipitation estimates on the accuracy of flash flood detection. J. Hydrol. in press.
Zhang, Y., Seo, D.-J., 2016. Recursive Estimators of Mean-areal and Local Bias in Precipitation Products that Account for Conditional Bias. Adv. Water Resour. in review.
Lee, H., Seo, D.-J., Zhang, Y., 2015. Utilizing satellite precipitation estimates for streamflow forecasting via adjustment of mean field bias in precipitation data and assimilation of streamflow. J. of Hydrol. 529, 779-794.
Rafieeinasab, A., Norouzi, A., Kim, S., Habibi, H., Nazari, B., Seo, D.-J., Lee, H., Cosgrove, B., Cui, Z., 2015. Toward high-resolution flash flood prediction in large urban areas – Analysis of sensitivity to spatiotemporal resolution of rainfall input and hydrologic modeling. J. Hydrol. Hydrologic Applications of Weather Radar 531, Part 2, 370–388.
Rafieeinasab, A., Norouzi, A., Seo, D.-J., Nelson, B., 2015. Improving high-resolution quantitative precipitation estimation via fusion of multiple radar-based precipitation products. J. Hydrol. 531, 320–336.
Zhang, Y., Seo, D.-J., Habib, E., McCollum, J., 2015. Differences in scale-dependent, climatological variation of mean areal precipitation based on satellite and radar-gauge observations. J. Hydrol. 522, 35–48. doi:10.1016/j.jhydrol.2014.11.077
Brown, J.D., Wu, L., He, M., Regonda, S., Lee, H., Seo, D.-J., 2014. Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 1. Experimental design and forcing verification. J. Hydrol. 519, Part D, 2869–2889.
Brown, J.D., He, M., Regonda, S., Wu, L., Lee, H., Seo, D.-J., 2014. Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 2. Streamflow verification. J. Hydrol. 519, Part D, 2847–2868.
Kim, S., Seo, D.-J., Riazim H., Shin, C., 2014. Improving water quality forecasting via data assimilation – Application of maximum likelihood ensemble filter to HSPF, J. Hydrol. 519(D), 2797-2809.
Rafieeinasab, A., Seo, D.-J., Lee, H., Kim, S., 2014. Comparative evaluation of maximum likelihood ensemble filter and ensemble Kalman filter for real-time assimilation of streamflow data into operational hydrologic models. J. Hydrol. 519, Part D, 2663–2675.
Seo, D.-J., Liu, Y., Moradkhani, H., Weerts, A., 2014. Ensemble prediction and data assimilation for operational hydrology (Editorial). J. Hydrol. Special Issue on Ensemble Prediction and Data Assimilation for Operational Hydrology, 519, 2661–2662.
Seo, D., Siddique, R., Ahnert, P., 2014. Objective Reduction of Rain Gauge Network via Geostatistical Analysis of Uncertainty in Radar-Gauge Precipitation Estimation. J. Hydrol. Eng. 20, 04014050. doi:10.1061/(ASCE)HE.1943-5584.0000969
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.
Seo D-J. 2013. Conditional bias-penalized kriging. Stochastic Environmental Research and Risk Assessment, January 2013, 27(1), 43-58.