Editorial Type:
Article
Article Type:
Research Article

Insights into Hydrometeorological Factors Constraining Flood Prediction Skill during the May and October 2015 Texas Hill Country Flood Events

Peirong Lin Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas

Search for other papers by Peirong Lin in
Current site
Google Scholar
PubMed Close
,
Larry J. Hopper Jr. NOAA/NWS Austin/San Antonio Weather Forecast Office, New Braunfels, Texas

Search for other papers by Larry J. Hopper Jr. in
Current site
Google Scholar
PubMed Close
,
Zong-Liang Yang Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas, and Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

Search for other papers by Zong-Liang Yang in
Current site
Google Scholar
PubMed Close
,
Mark Lenz NOAA/NWS Austin/San Antonio Weather Forecast Office, New Braunfels, Texas

Search for other papers by Mark Lenz in
Current site
Google Scholar
PubMed Close
, and
Jon W. Zeitler NOAA/NWS Austin/San Antonio Weather Forecast Office, New Braunfels, Texas

Search for other papers by Jon W. Zeitler in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Aug 2018
DOI:
https://doi.org/10.1175/JHM-D-18-0038.1
Page(s):
1339–1361
Restricted access

Abstract

This study evaluates the May and October 2015 flood prediction skill of a physically based model resembling the U.S. National Water Model (NWM) over the Texas Hill Country. It also investigates hydrometeorological factors that contributed to a record flood along the Blanco River at Wimberley (WMBT2) in May 2015. Using two radar-based quantitative precipitation estimation (QPE) products—Stage IV and Multi-Radar Multi-Sensor (MRMS)—it is shown that the event precipitation accuracy dominates the prediction skill, where the finer-resolution MRMS QPE mainly benefits basins with small drainage areas. Overall, the model exhibits good performance at gauges with fast flood response from causative rainfall and gauges that are not forecast points in the National Weather Service’s Advanced Hydrometeorological Prediction System, showing great promise for forecasts, warnings, and emergency response. However, the model suffers from poor prediction skill over regions without rapid flood response and regions with human-altered flows, suggesting the need to revisit the channel routing algorithm and incorporate modules to represent human alterations. Two contrasting flood events at WMBT2 with similar meteorological characteristics are examined in greater detail, revealing that the location of intense rainfall combined with land physiographic features are key to the flood response differences. Model sensitivity tests further show the record flood peak could be better obtained by tuning the deep-layer soil wetness and the flow velocity field in the river network, which offers hydrometeorological insights into the causes and the complex nature of such a flood and why the model struggles to predict the record flood peak.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-18-0038.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Zong-Liang Yang, liang@jsg.utexas.edu

Abstract

This study evaluates the May and October 2015 flood prediction skill of a physically based model resembling the U.S. National Water Model (NWM) over the Texas Hill Country. It also investigates hydrometeorological factors that contributed to a record flood along the Blanco River at Wimberley (WMBT2) in May 2015. Using two radar-based quantitative precipitation estimation (QPE) products—Stage IV and Multi-Radar Multi-Sensor (MRMS)—it is shown that the event precipitation accuracy dominates the prediction skill, where the finer-resolution MRMS QPE mainly benefits basins with small drainage areas. Overall, the model exhibits good performance at gauges with fast flood response from causative rainfall and gauges that are not forecast points in the National Weather Service’s Advanced Hydrometeorological Prediction System, showing great promise for forecasts, warnings, and emergency response. However, the model suffers from poor prediction skill over regions without rapid flood response and regions with human-altered flows, suggesting the need to revisit the channel routing algorithm and incorporate modules to represent human alterations. Two contrasting flood events at WMBT2 with similar meteorological characteristics are examined in greater detail, revealing that the location of intense rainfall combined with land physiographic features are key to the flood response differences. Model sensitivity tests further show the record flood peak could be better obtained by tuning the deep-layer soil wetness and the flow velocity field in the river network, which offers hydrometeorological insights into the causes and the complex nature of such a flood and why the model struggles to predict the record flood peak.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-18-0038.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Zong-Liang Yang, liang@jsg.utexas.edu

Supplementary Materials

    • Supplemental Materials (ZIP 2.39 MB)
Save
Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Ashley, S. T., and W. S. Ashley, 2008: Flood fatalities in the United States. J. Appl. Meteor. Climatol., 47, 805–818, https://doi.org/10.1175/2007JAMC1611.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bedient, P. P., and W. C. Huber, 1988: Hydrology and Floodplain Analysis. Addison-Wesley, 650 pp.

  • Beven, K., 2006: A manifesto for the equifinality thesis. J. Hydrol., 320, 18–36, https://doi.org/10.1016/j.jhydrol.2005.07.007.

  • Boucher, O., and Coauthors, 2013: Clouds and aerosols. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds. Cambridge University Press, 571–657.

  • Braud, I., H. Roux, S. Anquetin, M. Maubourguet, C. Manus, P. Viallet, and D. Dartus, 2010: The use of distributed hydrological models for the Gard 2002 flash flood event: Analysis of associated hydrological processes. J. Hydrol., 394, 162–181, https://doi.org/10.1016/j.jhydrol.2010.03.033.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cai, X., Z.-L. Yang, C. H. David, G.-Y. Niu, and M. Rodell, 2014: Hydrological evaluation of the Noah-MP land surface model for the Mississippi River Basin. J. Geophys. Res. Atmos., 119, 23–38, https://doi.org/10.1002/2013JD020792.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cho, Y., and B. A. Engel, 2017: NEXRAD quantitative precipitation estimations for hydrologic simulation using a hybrid hydrologic model. J. Hydrometeor., 18, 25–47, https://doi.org/10.1175/JHM-D-16-0013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cocks, S. B., S. M. Martinaitis, B. Kaney, J. Zhang, and K. Howard, 2016: MRMS QPE performance during the 2013/14 cool season. J. Hydrometeor., 17, 791–810, https://doi.org/10.1175/JHM-D-15-0095.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Corfidi, S. F., J. H. Merritt, and J. M. Fritsch, 1996: Predicting the movement of mesoscale convective complexes. Wea. Forecasting, 11, 41–46, https://doi.org/10.1175/1520-0434(1996)011<0041:PTMOMC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • David, C. H., D. R. Maidment, G.-Y. Niu, Z.-L. Yang, F. Habets, and V. Eijkhout, 2011: River network routing on the NHDPlus dataset. J. Hydrometeor., 12, 913–934, https://doi.org/10.1175/2011JHM1345.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Downton, M. W., J. Z. B. Miller, and R. A. Pielke Jr., 2005: Reanalysis of U.S. National Weather Service flood loss database. Nat. Hazards Rev., 6, 13–22, https://doi.org/10.1061/(ASCE)1527-6988(2005)6:1(13).

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Emanuel, K., 2017: Assessing the present and future probability of Hurricane Harvey’s rainfall. Proc. Natl. Acad. Sci. USA, 114, 12 681–12 684, https://doi.org/10.1073/pnas.1716222114.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Furl, C., H. O. Sharif, A. E. Hassan, N. Mazari, D. Burtch, and G. L. Mullendore, 2015: Hydrometeorological analysis of Tropical Storm Hermine and central Texas flash flooding, September 2010. J. Hydrometeor., 16, 2311–2327, https://doi.org/10.1175/JHM-D-14-0146.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Furl, C., H. Sharif, J. W. Zeitler, A. E. Hassan, and J. Joseph, 2018: Hydrometeorology of the catastrophic Blanco river flood in South Texas, May 2015. J. Hydrol. Reg. Stud., 15, 90–104, https://doi.org/10.1016/j.ejrh.2017.12.001.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gochis, D. J., W. Yu, and D. N. Yates, 2015: The WRF-Hydro model technical description and user’s guide, version 3.0. NCAR Tech. Doc., 120 pp., http://www.ral.ucar.edu/projects/wrf_hydro/.

  • Grice, G. K., and R. A. Maddox, 1983: Synoptic characteristics of heavy rainfall events in South Texas. Natl. Wea. Dig., 8, 8–16.

  • Gupta, H. V., H. Kling, K. K. Yilmaz, and G. F. Martinez, 2009: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hershfield, D. M., 1961: Rainfall frequency atlas of the United States: For durations from 30 minutes to 24 hours and return periods from 1 to 100 years. U.S. Weather Bureau Technical Paper 40, 65 pp., http://www.nws.noaa.gov/oh/hdsc/PF_documents/TechnicalPaper_No40.pdf.

  • Hopper, L. J., Jr., and N. L. Hampshire, 2016: Rainfall distributions for varying environmental conditions along the Balcones Escarpment, Texas. 30th Conf. on Hydrology, New Orleans, LA, Amer. Meteor. Soc., 1.3, https://ams.confex.com/ams/96Annual/webprogram/Paper281700.html.

  • Kitzmiller, D., D. Miller, R. Fulton, and F. Ding, 2013: Radar and multisensor precipitation estimation techniques in National Weather Service hydrologic operations. J. Hydrol. Eng., 18, 133–142, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000523.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Koster, R. D., Z. Guo, R. Yang, P. A. Dirmeyer, K. Mitchell, and M. J. Puma, 2009: On the nature of soil moisture in land surface models. J. Climate, 22, 4322–4335, https://doi.org/10.1175/2009JCLI2832.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Leopold, L. B., 1991: Lag times for small drainage basins. Catena, 18, 157–171, https://doi.org/10.1016/0341-8162(91)90014-O.

  • Li, H., M. S. Wigmosta, H. Wu, M. Huang, Y. Ke, A. M. Coleman, and L. R. Leung, 2013: A physically based runoff routing model for land surface and earth system models. J. Hydrometeor., 14, 808–828, https://doi.org/10.1175/JHM-D-12-015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lin, P., M. A. Rajib, Z.-L. Yang, M. Somos-Valenzuela, V. Merwade, D. R. Maidment, Y. Wang, and L. Chen, 2017: Spatio-temporal evaluation of simulated evapotranspiration and streamflow over Texas using the WRF-Hydro-RAPID framework. J. Amer. Water Resour. Assoc., 54, 40–54, https://doi.org/10.1111/1752-1688.12585.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lin, P., Z.-L. Yang, D. J. Gochis, W. Yu, D. R. Maidment, M. A. Somos-Valenzuela, and C. H. David, 2018: Implementation of a vector-based river network routing model in the community WRF-Hydro system for flood discharge simulation. Environ. Modell. Software, 107, https://doi.org/10.1016/j.envsoft.2018.05.018.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lin, Y., 2011: GCIP/EOP Surface: Precipitation NCEP/EMC 4KM Gridded Data (GRIB) Stage IV Data. Version 1.0, UCAR/NCAR Earth Observing Laboratory, accessed 5 August 2018, http://data.eol.ucar.edu/dataset/21.093.

  • Looper, J. P., and B. E. Vieux, 2012: An assessment of distributed flash flood forecasting accuracy using radar and rain gauge input for a physics-based distributed hydrologic model. J. Hydrol., 412–413, 114–132, https://doi.org/10.1016/j.jhydrol.2011.05.046.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Maddox, R. A., C. F. Chappell, and L. R. Hoxit, 1979: Synoptic and meso-α scale aspects of flash flood events. Bull. Amer. Meteor. Soc., 60, 115–123, https://doi.org/10.1175/1520-0477-60.2.115.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Maidment, D. R., 2016: Conceptual framework for the National Flood Interoperability Experiment. J. Amer. Water Resour. Assoc., 53, 245–257, https://doi.org/10.1111/1752-1688.12474.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • McDonnell, J. J., and K. Beven, 2014: Debates—The future of hydrological sciences: A (common) path forward? A call to action aimed at understanding velocities, celerities and residence time distributions of the headwater hydrograph. Water Resour. Res., 50, 5342–5350, https://doi.org/10.1002/2013WR015141.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mei, Y., E. N. Anagnostou, D. Stampoulis, E. I. Nikolopoulos, M. Borga, and H. J. Vegara, 2014: Rainfall organization control on the flood response of mild-slope basins. J. Hydrol., 510, 565–577, https://doi.org/10.1016/j.jhydrol.2013.12.013.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Merkel, W. H., 2002: Muskingum-Cunge flood routing procedure in NRCS hydrologic models. Second Federal Interagency Hydrologic Modeling Conf., Las Vegas, NV, Advisory Committee on Water Information, 12 pp., https://www.hydrocad.net/pdf/Merkel-MC-paper.pdf.

  • Milly, P. C. D., J. Betancourt, M. Falkenmark, R. M. Hirsch, Z. W. Kundzewicz, D. P. Lettenmaier, and R. J. Stouffer, 2008: Stationarity is dead: Whither water management? Science, 319, 573–574, https://doi.org/10.1126/science.1151915.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Neitsch, S. L., J. G. Arnold, J. R. Kiniry, R. Srinivasan, and J. R. Williams, 2009: Soil and water assessment tool theoretical documentation, version 2009. Texas Water Resources Institute Tech. Rep. 406, 618 pp., http://oaktrust.library.tamu.edu/handle/1969.1/128050.

  • Niu, G.-Y., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • NWS, 2015: Exceedance probability analysis for the Central Texas rainfall events of late October 2015. Tech. Doc., 6 pp., ftp://hdsc.nws.noaa.gov/pub/hdsc/data/aep/201510_Texas/AEP_TX_201510.pdf.

  • NWS, 2016a: Weather fatalities 2016. NWS Office of Climate, Water, and Weather Services, http://www.nws.noaa.gov/om/hazstats.shtml.

  • NWS, 2016b: Exceedance probability analysis for the San Antonio Area rainfall event of 23-24 May 2015. Tech. Doc., 5 pp., ftp://hdsc.nws.noaa.gov/pub/hdsc/data/aep/201505_Texas/AEP_TX_201505.pdf.

  • Pielke, R. A., Jr., M. W. Downton, and J. B. Miller, 2002: Flood damage in the United States, 1926–2000: A reanalysis of National Weather Service estimates. NCAR Doc., 96 pp., http://sciencepolicy.colorado.edu/flooddamagedata/flooddamagedata.pdf.

  • Qi, Y., S. Martinaitis, J. Zhang, and S. Cocks, 2016: A real-time automated quality control of hourly rain gauge data based on multiple sensors in MRMS system. J. Hydrometeor., 17, 1675–1691, https://doi.org/10.1175/JHM-D-15-0188.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rajib, A. M., V. Merwade, and Z. Yu, 2016: Multi-objective calibration of a hydrologic model using spatially distributed remotely sensed/in-situ soil moisture. J. Hydrol., 536, 192–207, https://doi.org/10.1016/j.jhydrol.2016.02.037.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Reges, H. W., N. Doesken, J. Turner, N. Newman, A. Bergantino, and Z. Schwalbe, 2016: CoCoRaHS: The evolution and accomplishments of a volunteer rain gauge network. Bull. Amer. Meteor. Soc., 97, 1831–1846, https://doi.org/10.1175/BAMS-D-14-00213.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rogers, R. F., and Coauthors, 2017: Rewriting the tropical record books: The extraordinary intensification of Hurricane Patricia (2015). Bull. Amer. Meteor. Soc., 98, 2091–2112, https://doi.org/10.1175/BAMS-D-16-0039.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Saharia, M., P.-E. Kirstetter, H. Vergara, J. J. Gourley, Y. Hong, and M. Giroud, 2017: Mapping flash flood severity in the United States. J. Hydrometeor., 18, 397–411, https://doi.org/10.1175/JHM-D-16-0082.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Schaake, J. C., V. I. Koren, Q.-Y. Duan, K. Mitchell, and F. Chen, 1996: Simple water balance model for estimating runoff at different spatial and temporal scales. J. Geophys. Res., 101, 7461–7475, https://doi.org/10.1029/95JD02892.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Schroeder, A., J. Basara, J. M. Shepherd, and S. Nelson, 2016: Insights into atmospheric contributors to urban flash flooding across the United States using an analysis of rawinsonde data and associated calculated parameters. J. Appl. Meteor. Climatol., 55, 313–323, https://doi.org/10.1175/JAMC-D-14-0232.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Schumacher, R. S., and R. H. Johnson, 2005: Organization and environmental properties of extreme-rain-producing mesoscale convective systems. Mon. Wea. Rev., 133, 961–976, https://doi.org/10.1175/MWR2899.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Schumacher, R. S., and R. H. Johnson, 2009: Quasi-stationary, extreme-rain-producing convective systems associated with midlevel cyclonic circulations. Wea. Forecasting, 24, 555–574, https://doi.org/10.1175/2008WAF2222173.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sharif, H. O., A. A. Hassan, S. Bin-Shafique, H. Xie, and J. Zeitler, 2010: Hydrologic modeling of an extreme flood in the Guadalupe River in Texas. J. Amer. Water Resour. Assoc., 46, 881–891, https://doi.org/10.1111/j.1752-1688.2010.00459.x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sivapalan, M., 2018: From engineering hydrology to Earth system science: Milestones in the transformation of hydrologic science. Hydrol. Earth Syst. Sci., 22, 1665–1693, https://doi.org/10.5194/hess-22-1665-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Smith, J. A., M. Baeck, J. E. Morrison, and P. Sturdevant-Rees, 2000: Catastrophic rainfall and flooding in Texas. J. Hydrometeor., 1, 5–25, https://doi.org/10.1175/1525-7541(2000)001<0005:CRAFIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Smith, M. B., and Coauthors, 2012: Results of the DMIP 2 Oklahoma experiments. J. Hydrol., 418–419, 17–48, https://doi.org/10.1016/j.jhydrol.2011.08.056.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Todini, E., 2007: A mass conservative and water storage consistent variable parameter Muskingum-Cunge approach. Hydrol. Earth Syst. Sci., 11, 1645–1659, https://doi.org/10.5194/hess-11-1645-2007.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bull. Amer. Meteor. Soc., 84, 1205–1217, https://doi.org/10.1175/BAMS-84-9-1205.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Veilleux, A. G., T. A. Cohn, K. M. Flynn, R. R. Mason Jr., and P. R. Hummel, 2014, Estimating magnitude and frequency of floods using the PeakFQ 7.0 program. USGS Fact Sheet 2013-3108, 2 pp., http://pubs.usgs.gov/fs/2013/3108/.

  • Xia, Y., and Coauthors, 2012: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J. Geophys. Res., 117, D03109, https://doi.org/10.1029/2011JD016048.

    • Search Google Scholar
    • Export Citation

  • Yang, Z.-L., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins. J. Geophys. Res., 116, D12110, https://doi.org/10.1029/2010JD015140.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, J., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 621–638, https://doi.org/10.1175/BAMS-D-14-00174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, Z., V. Koren, S. Reed, M. Smith, Y. Zhang, F. Moreda, and B. Cosgrove, 2012: SAC-SMA a priori parameter differences and their impact on distributed hydrologic model simulations. J. Hydrol., 420–421, 216–227, https://doi.org/10.1016/j.jhydrol.2011.12.004.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zheng, H., and Z.-L. Yang, 2016: Effects of soil-type datasets on regional terrestrial water cycle simulations under different climatic regimes, J. Geophys. Res. Atmos., 121, 14 387–14 402, https://doi.org/10.1002/2016JD025187.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zoccatelli, D., M. Borga, A. Viglione, G. B. Chirico, and G. Blöschl, 2011: Spatial moments of catchment rainfall: Rainfall spatial organisation, basin morphology, and flood response. Hydrol. Earth Syst. Sci., 15, 3767–3783, https://doi.org/10.5194/hess-15-3767-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1393 603 93
PDF Downloads 745 96 6