Hydrology and Water Resources Lab

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. The Hydrology and Water Resources Lab focuses on integrative hydrologic prediction and water resources information research for sustainable and resilient management and planning of water resources and hazards.

Personnel

  • Dong-Jun Seo, Ph.D., Lab Director
  • Sunghee Kim, Ph.D., Research Associate
  • Seongjin Noh, Ph.D., Research Assistant Professor

Students

  • Reza Ahmad Limon (Ph.D.)
  • Babak Alizadeh (Ph.D.)
  • Mohammad Nabatian (Ph.D.)
  • Fatemeh (Soona) Habibi Ardekani (Ph.D.)
  • Haojing (Ann) Shen (Ph.D.)

Publications

  • 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.
  • Zhang, Y., D.-J. Seo, and E. Habib, 2014. A Dissection of Differences in Scale-dependent, Climatological Variation of Mean Areal Precipitation Based on a Satellite and Radar-Gauge Observations. submitted to Journal of Hydrology.
  • 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 Special Issue on Ensemble Prediction and Data Assimilation for Operational Hydrology and Water Resources Management, Journal of Hydrology.
  • Lee, H., Y. Zhang, D.-J. Seo and P. Xie, Utilizing satellite precipitation estimates for operational streamflow forecasting via joint assimilation with streamflow observations, submitted to Special Issue on Ensemble Prediction and Data Assimilation for Operational Hydrology and Water Resources Management, 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 Special Issue on Ensemble Prediction and Data Assimilation for Operational Hydrology and Water Resources Management, 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 in Special Issue on Ensemble Prediction and Data Assimilation for Operational Hydrology and Water Resources Management, 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. (2014). "Objective Reduction of Rain Gauge Network via Geostatistical Analysis of Uncertainty in Radar-Gauge Precipitation Estimation." J. Hydrol. Eng. http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29HE.1943-5584.0000969
  • 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.
  • Seo D-J. 2013. Conditional bias-penalized kriging. Stochastic Environmental Research and Risk Assessment, January 2013, Volume 27, Issue 1, pp 43-58. 
  • Hou, D., M. Charles, Y. Luo, Z. Toth, Y. Zhu, R. Krzysztofowicz, Y. Lin, P. Xie, D.-J. Seo, M. Pena, and B. Cui. Climatology-Calibrated Precipitation Analysis at Fine Scales: Statistical Adjustment of STAGE IV towards CPC Gauge-Based Analysis, to appear in Journal of Hydrometeorology, doi: http://dx.doi.org/10.1175/JHM-D-11-0140.1.
  • Yuqiong Liu, Albrecht H. Weerts, Martyn Clark, Harrie-Jan Hendricks Franssen, Sujay Kumar, Hamid Moradkhani, Dong-Jun Seo, Dirk Schwanenberg, Paul Smith, Albert van Dijk, Nils van Velzen, Minxue He, Haksu Lee, Seong Jin Noh, Olda Rakovec, and Pedro Restrepo, 2012. Toward Advancing Data Assimilation in Operational Hydrologic Forecasting and Water Resources Management: A Review of the Current Status, Challenges, and Emerging Opportunities, Hydrol. Earth Syst. Sci. Discuss., 9, 3415-3472.
  • Lee, H., D.-J. Seo, Y. Liu, V. Koren, P. McKee, and R. Corby, 2012. Variational Assimilation of Streamflow into Operational Distributed Hydrologic Models: Effect of Spatiotemporal Adjustment Scale, Hydrol. Earth Syst. Sci. Discuss., 9, 93-138.
  • 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.
  • Brown, J. D. and D.-J. Seo, 2012. Evaluation of a nonparametric post-processor for bias correction and uncertainty estimation of hydrologic predictions, Hydrol. Process. Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/hyp.9263.
  • Habib, E., Qin, L., D.-J. Seo, G. Ciach, and B. R. Nelson, 2011. Independent Assessment of Incremental Complexity in the NWS Multi-Sensor Precipitation Estimator Algorithms, Journal of Hydrologic Engineering, doi: https://ascelibrary.org/doi/10.1061/%28ASCE%29HE.1943-5584.0000638.
  • Brown, J. B., J. Du, and D.-J. Seo, 2011. Verification of precipitation forecasts from NCEP's Short Range Ensemble Forecast (SREF) system with reference to hydrologic forecasting in lumped basins, Journal of Hydrometeorology, doi: http://dx.doi.org/10.1175/JHM-D-11-036.1.
  • Zhang, J., K. Howard, C. Langston, S. Vasiloff, B. Kaney, A. Arthur, S. V. Cooten, K. Kelleher, D. Kitzmiller, F. Dong, D.-J. Seo, E. Wells, M. Mullusky and C. Dempsey, 2011. National Mosaic and Multi-sensor QPE (NMQ) System – Description, Results and Future Plans, Bulletin of the American Meteorological Society, 92(10): 1322-1338, doi: http://dx.doi.org/10.1175/2011BAMS-D-11-00047.1.
  • Lee, H., D.-J. Seo, and V. Koren, 2011. Assimilation of streamflow and in-situ soil moisture data into operational distributed hydrologic models: Effects of uncertainty in the data and initial soil moisture conditions, Advances in Water Resources, 34, 1597-1615.
  • Wu, L., D.-J. Seo, J. Demargne, J. Brown, S. Cong, and J. Schaake, 2011. Generation of ensemble precipitation forecast from single-valued quantitative precipitation forecast via meta-Gaussian distribution-based models, J. Hydrol., 399(3-4), 281-298.
  • Liu, Y., J. Brown, J. Demargne, and D.-J. Seo, 2011. A wavelet-based approach to assessing timing errors in hydrologic predictions, J. Hydrol., 397(3-4), 210-224.
  • Demargne J., Brown J.D., Liu Y., Seo D-J., Wu L., Toth Z., and Zhu Y. (2010) Diagnostic verification of hydrometeorological and hydrologic ensembles. Atmospheric Science Letters, 11 (2), 114-122.
  • Brown, J.D., Demargne, J., Seo, D-J. and Liu, Y., 2010. The Ensemble Verification System (EVS): a software tool for verifying ensemble forecasts of hydrometeorological and hydrologic variables at discrete locations. Environmental Modelling and Software. 25(7), 854-872.
  • Brown, J.D. and Seo, D-J., 2010. A nonparametric post-processor for bias correcting ensemble forecasts of hydrometeorological and hydrologic variables. Journal of Hydrometeorology. 11(3), 642-665.
  • Nelson, B., D.-J. Seo, and D. Kim, 2010. Multisensor Precipitation Reanalysis. Journal of Hydrometeorology. 11(3), 666-682.
  • Kim, D., B. Nelson, and D.-J. Seo, 2009. Characteristics of Reprocessed Hydrometeorological Automated Data System (HADS) Hourly Precipitation Data. Weather and Forecasting, 24, 1287-1296.
  • Seo, D.-J., L. Cajina, R. Corby and T. Howieson, 2009: Automatic State Updating for Operational Streamflow Forecasting via Variational Data Assimilation, 367, Journal of Hydrology, 255-275.
  • Kuzmin, V., Seo, D.-J., Koren, V., 2008. Fast and efficient optimization of hydrologic model parameters using a priori estimates and stepwise line search. J. Hydrol. 353 (1–2), 109–128.
  • Vasiloff, S. V., D.-J. Seo, K. W. Howard, J. Zhang, D. H. Kitzmiller, M. G. Mullusky, W. F. Krajewski, E. A. Brandes, R. M. Rabin, D. S. Berkowitz, H. E. Brooks, J. A. McGinley, R. J. Kuligowski, B. G. Brown, 2007: Improving QPE and Very Short-Term QPF: An Initiative for a Community-wide Integrated Approach, Bulletin of the American Meteorological Society, December, 1899-1911.
  • Schaake J., J. Demargne, M. Mullusky, E. Welles, L. Wu, H. Herr, X. Fan, and D.-J. Seo, 2007: Precipitation and temperature ensemble forecasts from single-value forecasts, 4, Hydrology and Earth System Sciences, 655-717.
  • Seo, D.-J., H. Herr and J. Schaake, 2006. A statistical post-processor for accounting of hydrologic uncertainty in short-range ensemble streamflow prediction, Hydrol. Earth Syst. Sci. Discuss., 3, 1987-2035.
  • Koren, V., S. Reed, M. Smith, Z. Zhang, and D.-J. Seo, 2004: Hydrology laboratory research modeling system (HL-RMS) of the US national weather service. Journal of Hydrology, 291(3-4), 297-318.
  • Smith, M. B., D.-J. Seo, V. I. Koren, S. Reed, Z. Zhang, Q.-Y. Duan, F. Moreda, and S. Cong, 2004: The Distributed Model Intercomparison Project (DMIP): Motivation and experiment design. Journal of Hydrology (DMIP special issue), 298(1-4), 4-26. 
  • Reed, S., V. Koren, M. Smith, Z. Zhang, F. Moreda, D.-J. Seo, and DMIP participants, 2004: Overall Distributed Model Intercomparison Project results. Journal of Hydrology (DMIP special issue), 298(1-4), 27-60.
  • Georgakakos, K., D.-J. Seo, H. Gupta, J. Schaake, and M. B. Butts, 2004: Towards the characterization of streamflow simulation uncertainty through multimodel ensembles. Journal of Hydrology (DMIP special issue), 298(1-4), 222-241.
  • Seo, D.-J., V. Koren, and N. Cajina, 2003: Real-time variational assimilation of hydrologic and hydrometeorological data into operational hydrologic forecasting. Journal of Hydrometeorology, 4, 627-641.
  • Seo, D.-J. And J. P. Breidenbach, Real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements, J. Hydrometeorol., 3, 93-111, 2002.
  • Seo, D.-J., S. Perica, E. Welles, and J. Schaake, Simulation precipitation fields from Probabilistic Quantitative Precipitation Forecast, J. Hydrol., 239, 203-229, 2000.
  • Seo, D.-J., J. P. Breidenbach, R. A .Fulton, D. A. Miller, and T. O’Bannon, Real-time adjustment of range-dependent biases in WSR-88D rainfall data due to nonuniform vertical profile of reflectivity, J. Hydrometeorol., 1(3), 222-240, 2000.
  • Seo, D.-J., J. P. Breidenbach, and E. R. Johnson, Real-time estimation of mean field bias in radar rainfall data, J. Hydrol., 233, 1999.
  • Koren, V. I., B. D. Finnerty, J. C. Schaake, M. B. Smith, D.-J. Seo, and Q.-Y. Duan, Scale dependencies of hydrologic models to spatial variability of precipitation, J. Hydrol., 217, 285-302, 1999.
  • Seo, D.-J., Real-time estimation of rainfall fields using radar rainfall and rain gage data, J. Hydrol., 208, 37-52, 1998b.
  • Seo, D.-J., Real-time estimation of rainfall fields using rain gage data under fractional coverage, J. Hydrol., 208, 25-36, 1998a.
  • Fulton, R. A., J. P. Breidenbach, D-J. Seo, D. A. Miller, and T. O’Bannon, The WSR-88D rainfall algorithm, Weather and Forecasting, 13, 377-395, 1998.
  • Anagnostou, E. N., W. F. Krajewski, D.-J. Seo, and E. R. Johnson, Mean-field radar rainfall bias studies for NEXRAD, ASCE J. Hydrol. Eng., 3(3), 149-159, 1998.
  • Finnerty, B. D., M. B. Smith, D.-J. Seo, V. Koren, and G. Moglen, Space-time scale sensitivity of the Sacramento model to radar-gage precipitation inputs, J. Hydrol., 203, 1997.
  • Seo, D.-J. and J. A. Smith, On the Relationship between Catchment Scale and Climatological Variability of Areal Runoff Volume, Water Res. Resour., 32(3), 1996b.
  • Seo, D.-J. and J. A. Smith, Characterization of the Climatological Variability of Mean Areal Rainfall Through Fractional Coverage, Water Res. Resour., 32(7), 1996a.
  • Smith, J. A., D.-J. Seo, M. L. Baeck and M. D. Hudlow, An intercomparison study of NEXRAD Precipitation Estimates, Water Res. Resour., 32(7), 1996.
  • Seo, D.-J., Nonlinear Estimation of Spatial Distribution of Rainfall - An Indicator Cokriging, Stoch. Hydrol. Hydraul., 10(2), 1996.
  • Seo, D.-J. and J. A. Smith, Radar-Based Short-Term Rainfall Prediction, J. Hydrol., 131, 1992.
  • Seo, D.-J. and J. A. Smith, Rainfall Estimation Using Rain Gages and Radar ‑ A Bayesian Approach 2. An Application, Stoch. Hydrol. Hydraul., 5(2), 1991b.
  • Seo, D.-J. and J. A. Smith, Rainfall Estimation Using Rain Gages and Radar ‑ A Bayesian Approach 1. Derivation of Estimators, Stoch. Hydrol. Hydraul., 5(2), 1991a.
  • Seo, D.-J., W. F. Krajewski and D. S. Bowles, Stochastic Interpolation of Co‑Kriging 2. Results, Water Resour. Res., 26(5), 1990.
  • Seo, D.-J., W. F. Krajewski and D. S. Bowles, Stochastic Interpolation of Co‑Kriging 1. Design of Experiments, Water Resour. Res., 26(3), 1990.
  • Azimi‑Zonooz, A., W. F. Krajewski, D. S. Bowles and D.‑J. Seo, Spatial Rainfall Estimation by Linear and Non‑Linear Co‑Kriging of Radar‑Rainfall and Raingage Data, Stoch. Hydrol. Hydraul., 3, 1989.
    • Bras, R. L. and D.‑J. Seo, Irrigation Control in the Presence of Salinity ‑ Extended Linear Quadratic Approach, Water Resour. Res., 23(7), 1987.

Other Peer-Reviewed Journal Papers

  • Seo, D.-J., K. Hwang and D.-R. Lee, Current state and future of radar-based flood forecasting, Institute of Eletronics Engineers of Korea Magazine, 40(2), 57-63.
  • Habib, E., L. Qin and D.-J. Seo. Comparison of Different Radar-Gauge Merging Techniques in the NWS Multi-sensor Precipitation Estimator Algorithm, accepted for publication in Weather Radar and Hydrology Special Issue, IAHS Publ. 351 (Red Books).
  • Seo, D.-J., Toward operational hydrologic ensemble forecasting across weather and climate scales, to appear in proceedings for WIRADA Science Symposium, 1-5 August 2011, Melbourne, Australia.
  • Demargne, J., L. Wu, D.-J. Seo, and J. Schaake, Experimental hydrometeorological and hydrologic ensemble forecasts and their verification in the U.S. National Weather Service, IAHS Publications Series (Red Books), 313, 177-187, 2007.
  • Seo, D.-J., V. Koren, and S. Reed, Improving a priori estimates of hydraulic parameters in a distributed routing model via variational assimilation of long-term streamflow data. IAHS Publications Series (Red Books) 282, 138-142, 2003.
  • Seo, D.-J., V. Koren, and L. Cajina, Real-time assimilation of radar-based precipitation data and streamflow observations into a distributed hydrologic model. IAHS Publications Series (Red Books) 282, 109-113, 2003.
  • Seo, D.-J., On state estimation using remotely sensed data and ground measurements – An overview of some useful tools, J. Korean Soc. Remote Sensing, 7(1), 45-67, 1991.

Book Chapters

  • Seo, D.-J., A. Seed, and G. Delrieu, 2010. Radar-based rainfall estimation, chapter in AGU Book Volume on Rainfall: State of the Science, F. Testik and M. Gebremichael, Editors.
  • Weerts, A., D.-J. Seo, M. Werner, and J. C. Schaake, Operational hydrologic ensemble forecasting, chapter to appear in Applied Uncertainty Analysis for Flood Risk Management, K. Beven and J. Hall, Editors.

Tools and Data

  • Flash flood forecast system for DFW
  • Hydrology Laboratory Research Distributed Hydrologic Model (HLRDHM)
  • Maximum likelihood ensemble filter for HSPF (MLEF-HSPF)
  • Conditional bias-penalized kriging (CBPK)
  • Conditional bias-penalized Kalman filter (CBPKF)
  • Hydrologic information system (UTA-HIS)
  • Ensemble Post-Processor (EnsPost)
  • Meteorological Ensemble Precipitation Processor (MEFP)
  • Multisensor Precipitation Estimator (MPE)
  • Adjoint-Based OPTimizer (AB-OPT)
  • Maximum likelihood ensemble filter for lumped SAC-UH (MLEF-SACUH)
  • 2-D variational assimilator for SAC-UH (2DVAR)
  • 4-D variational assimilator for gridded SAC-kinematic wave routing (4DVAR)
  • 1-D variational assimilator for 3-parameter Muskingum routing (4DVAR)
  • Ensemble Precipitation Processor for space-time simulation of precipitation from PQPF (EPP)