• Arnault, J., S. Wagner, T. Rummler, B. Fersch, J. Bliefernicht, S. Andresen, and H. Kunstmann, 2016: Role of runoff–infiltration partitioning and resolved overland flow on land–atmosphere feedbacks: A case study with the WRF-Hydro coupled modeling system for West Africa. J. Hydrometeor., 17, 14891516, https://doi.org/10.1175/JHM-D-15-0089.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arnault, J., and et al. , 2018: Precipitation sensitivity to the uncertainty of terrestrial water flow in WRF-Hydro: An ensemble analysis for central Europe. J. Hydrometeor., 19, 10071025, https://doi.org/10.1175/JHM-D-17-0042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arnault, J., and et al. , 2019: A joint soil-vegetation-atmospheric water tagging procedure with WRF-Hydro: Implementation and application to the case of precipitation partitioning in the Upper Danube River Basin. Water Resour. Res., 55, 62176243, https://doi.org/10.1029/2019WR024780.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berne, A., and W. F. Krajewski, 2013: Radar for hydrology: Unfulfilled promise or unrecognized potential? Adv. Water Resour., 51, 357366, https://doi.org/10.1016/j.advwatres.2012.05.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., and V. Chandrasekar, 2001: Polarimetric Doppler Weather Radar: Principles and Applications. Cambridge University Press, 636 pp.

  • Chandrasekar, V., H. Chen, and B. Philips, 2018: Principles of high-resolution radar network for hazard mitigation and disaster management in an urban environment. J. Meteor. Soc. Japan, 96A, 119139, https://doi.org/10.2151/jmsj.2018-015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chao, L., K. Zhang, Z. Yang, J. Wang, P. Lin, J. Liang, Z. Li, and Z. Gu, 2020: Improving flood simulation capability of the WRF-Hydro-RAPID model using a multi-source precipitation merging method. J. Hydrol., 592, 125814, https://doi.org/10.1016/j.jhydrol.2020.125814.

    • Search Google Scholar
    • Export Citation
  • Chen, H., R. Cifelli, and A. White, 2020: Improving operational radar rainfall estimates using profiler observations over complex terrain in northern California. IEEE Trans. Geosci. Remote Sens., 58, 18211832, https://doi.org/10.1109/TGRS.2019.2949214.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cifelli, R., V. Chandrasekar, H. Chen, and L. E. Johnson, 2018: High resolution radar quantitative precipitation estimation in the San Francisco Bay Area: Rainfall monitoring for the urban environment. J. Meteor. Soc. Japan, 96A, 141155, https://doi.org/10.2151/jmsj.2018-016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cristiano, E., M. C. ten Veldhuis, and N. van de Giesen, 2017: Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas—A review. Hydrol. Earth Syst. Sci., 21, 38593878, https://doi.org/10.5194/hess-21-3859-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dugger, A. L., and et al. , 2017: Learning from the National Water Model: Regional improvements in streamflow prediction through experimental parameter and physics updates to the WRF-Hydro Community Model. 31st Conf. on Hydrology, Seattle, WA, Amer. Meteor. Soc., 6A.3, https://ams.confex.com/ams/97Annual/webprogram/Paper314352.html.

  • Ek, M. B., and et al. , 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, https://doi.org/10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Garcia, R., and R. A. Kahawita, 1986: Numerical solution of the St. Venant equations with the MacCormack finite-difference schemes. Int. J. Numer. Methods Fluids, 6, 259274, https://doi.org/10.1002/fld.1650060502.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gochis, D. J., and et al. , 2018: The WRF-Hydro modeling system technical description (version 5.0). NCAR Tech. Note, 107 pp., https://ral.ucar.edu/sites/default/files/public/WRF-HydroV5TechnicalDescription.pdf.

  • 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, 8091, https://doi.org/10.1016/j.jhydrol.2009.08.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gustafsson, N., and et al. , 2018: Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres. Quart. J. Roy. Meteor. Soc., 144, 12181256, https://doi.org/10.1002/qj.3179.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hatchett, B., and et al. , 2020: Observations of an extreme atmospheric river storm with a diverse sensor network. Earth Space Sci., 6, e2020EA001129, https://doi.org/10.1029/2020EA001129.

    • Search Google Scholar
    • Export Citation
  • Johnson, J. M., D. Munasinghe, D. Eyelade, and S. Cohen, 2019: An integrated evaluation of the National Water Model (NWM)–Height Above Nearest Drainage (HAND) flood mapping methodology. Nat. Hazards Earth Syst. Sci., 19, 24052420, https://doi.org/10.5194/nhess-19-2405-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, L. E., R. C. Ifelli, and A. B. White, 2020: Benefits of an advanced quantitative precipitation information system. J. Flood Risk Manage., 13, e12573, https://doi.org/10.1111/jfr3.12573.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jordan, R, 1991: one-dimensional temperature model for a snow cover: Technical documentation for SNTERERM.89. Special Rep. 91-16, Cold Region Research and Engineers Laboratory, U.S. Army Corps of Engineers, Hanover, NH, 61 pp.

  • Krajewski, W. F., and J. A. Smith, 2002: Radar hydrology: Rainfall estimation. Adv. Water Resour., 25, 13871394, https://doi.org/10.1016/S0309-1708(02)00062-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lahmers, T. M., H. Gupta, C. L. Castro, D. J. Gochis, D. Yates, A. Dugger, D. Goodrich, and P. Hazenberg, 2019: Enhancing the structure of the WRF-Hydro hydrologic model for semiarid environments. J. Hydrometeor., 20, 691714, https://doi.org/10.1175/JHM-D-18-0064.1.

    • 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 scheme in the community WRF-Hydro modeling framework for flood discharge simulation. Environ. Modell. Software, 107, 111, https://doi.org/10.1016/j.envsoft.2018.05.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., J. Liu, C. Li, F. Yu, W. Wang, and Q. Qiu, 2021: Parameter sensitivity analysis of the WRF-Hydro modeling system for streamflow simulation: A case study in semi-humid and semi-arid catchments of northern China. Asia Pac. J. Atmos. Sci., 57, 451466, https://doi.org/10.1007/s13143-020-00205-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, Y., and V. Chandrasekar, 2020: A hierarchical Bayesian approach for bias correction of NEXRAD dual-polarization rainfall estimates: Case study on Hurricane Irma in Florida. IEEE Geosci. Remote Sens. Lett., 18, 568572, https://doi.org/10.1109/LGRS.2020.2983041.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Naabil, E., B. L. Lamptey, J. Arnault, A. Olufayo, and H. Kunstmann, 2017: Water resources management using the WRF-Hydro modelling system: Case-study of the Tono Dam in West Africa. J. Hydrol. Reg. Stud., 12, 196209, https://doi.org/10.1016/j.ejrh.2017.05.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G., and et al. , 2011: The community Noah land surface model with multi-parameterization 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
  • Ralph, F. M., and et al. , 2016: CalWater field studies designed to quantify the roles of Atmospheric Rivers and aerosols in modulating U.S. West Coast precipitation in a changing climate. Bull. Amer. Meteor. Soc., 97, 12091228, https://doi.org/10.1175/BAMS-D-14-00043.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rummler, T., J. Arnault, D. J. Gochis, and H. Kunstmann, 2019: Role of lateral terrestrial water flow on the regional water cycle in a complex Terrain region: Investigation with a fully coupled model system. J. Geophys. Res. Atmos., 124, 507529, https://doi.org/10.1029/2018JD029004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sellers, P. J., M. D. Heiser, and F. G. Hall, 1992: Relations between surface conductance and spectral vegetation indices at intermediate (100 m2 to 15 km2) length scales. J. Geophys. Res., 97, 19 03319 059, https://doi.org/10.1029/92JD01096.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Senatore, A., G. Mendicino, D. J. Gochis, W. Yu, D. Yates, and H. Kunstmann, 2015: Full coupled atmosphere-hydrology simulations for the central Mediterranean: Impact of enhanced hydrological parameterization for short and long time scales. J. Adv. Model. Earth Syst., 7, 16931715, https://doi.org/10.1002/2015MS000510.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, D. J., E. Habib, H. Andrieu, and E. Morin, 2015: Hydrologic applications of weather radar. J. Hydrol., 531, 231233, https://doi.org/10.1016/j.jhydrol.2015.11.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sharma, A., C. Wasko, and D. P. Lettenmaier, 2018: If precipitation extremes are increasing, why aren’t floods? Water Resour. Res., 54, 85458551, https://doi.org/10.1029/2018WR023749.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tolson, B. A., and C. A. Shoemaker, 2007: Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resour. Res., 43, W01413, https://doi.org/10.1029/2005WR004723.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Viterbo, F., and et al. , 2020: A multiscale, hydrometeorological forecast evaluation of National Water Model forecasts of the May 2018 Ellicott City, Maryland, Flood. J. Hydrometeor., 21, 475499, https://doi.org/10.1175/JHM-D-19-0125.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wehbe, Y., and et al. , 2019: Analysis of an extreme weather event in a hyper-arid region using WRF-Hydro coupling, station, and satellite data. Nat. Hazards Earth Syst. Sci., 19, 11291149, https://doi.org/10.5194/nhess-19-1129-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, A. B., B. J. Moore, D. J. Gottas, and P. J. Neiman, 2019: Winter storm conditions leading to excessive runoff above California’s Oroville dam during January and February 2017. Bull. Amer. Meteor. Soc., 100, 5570, https://doi.org/10.1175/BAMS-D-18-0091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xia, Y., and et al. , 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 product. J. Geophys. Res., 117, D03109, https://doi.org/10.1029/2011JD016048.

    • Search Google Scholar
    • Export Citation
  • Yang, Z., X. Cai, G. Zhang, A. Tavakoly, Q. Jin, L. Meyer, and X. Guan, 2011: The Community Noah Land Surface Model with Multi-Parameterization Options (Noah-MP): Technical description. The University of Texas at Austin, 75 pp., https://www.jsg.utexas.edu/noah-mp/files/Noah-MP_Technote_v0.2.pdf .

  • Yucel, I., A. Onen, K. K. Yilmaz, and D. Gochis, 2015: Calibration and evaluation of a flood forecasting system: Utility of numerical weather prediction model, data assimilation and satellite-based rainfall. J. Hydrol., 523, 4966, https://doi.org/10.1016/j.jhydrol.2015.01.042.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., and et al. , 2011: National Mosaic and Multi-Sensor QPE (NMQ) system: Description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 13211338, https://doi.org/10.1175/2011BAMS-D-11-00047.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., P. Lin, S. Gao, and Z. Fang, 2020: Understanding the re-infiltration process to simulating streamflow in north central Texas using the WRF-Hydro modeling system. J. Hydrol., 587, 124902, https://doi.org/10.1016/j.jhydrol.2020.124902.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Quantifying the Potential of AQPI Gap-Filling Radar Network for Streamflow Simulation through a WRF-Hydro Experiment

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  • 1 a Colorado State University, Fort Collins, Colorado
  • | 2 b NOAA/Physical Sciences Laboratory, Boulder, Colorado
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Abstract

It remains a challenge to provide accurate and timely flood warnings in many parts of the western United States. As part of the Advanced Quantitative Precipitation Information (AQPI) project, this study explores the potential of using the AQPI gap-filling radar network for streamflow simulation of selected storm events in the San Francisco Bay Area under a WRF-Hydro modeling system. Two types of watersheds including natural and human-affected among the most flood-prone region of the Bay Area are investigated. Based on the high-resolution AQPI X-band radar rainfall estimates, three basic routing configurations, including Grid, Reach, and National Water Model (NWM), are used to quantify the impact of different model physics options on the simulated streamflow. It is found that the NWM performs better in terms of reproducing streamflow volumes and hydrograph shapes than the other routing configurations when reservoirs exist in the watershed. Additionally, the AQPI X-band radar rainfall estimates (without gauge correction) provide reasonable streamflow simulations, and they show better performance in reproducing the hydrograph peaks compared with the gauge-corrected rainfall estimates based on the operational S-band Next Generation Weather Radar network. Also, a sensitivity test reveals that surficial conditions have a significant influence on the streamflow simulation during the storm: the discharge increases to a higher level as the infiltration factor (REFKDT) decreases, and its peak goes down and lags as surface roughness coefficient (Mann) increases. The time delay analysis of precipitation input on the streamflow at the two outfalls of the surveyed watersheds further demonstrates the link between AQPI gap-filling radar observations and streamflow changes in this urban region.

© 2021 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: Y. Ma, yingzhao.ma@colostate.edu

This article is included in the 12th International Precipitation Conference (IPC12) Special Collection.

Abstract

It remains a challenge to provide accurate and timely flood warnings in many parts of the western United States. As part of the Advanced Quantitative Precipitation Information (AQPI) project, this study explores the potential of using the AQPI gap-filling radar network for streamflow simulation of selected storm events in the San Francisco Bay Area under a WRF-Hydro modeling system. Two types of watersheds including natural and human-affected among the most flood-prone region of the Bay Area are investigated. Based on the high-resolution AQPI X-band radar rainfall estimates, three basic routing configurations, including Grid, Reach, and National Water Model (NWM), are used to quantify the impact of different model physics options on the simulated streamflow. It is found that the NWM performs better in terms of reproducing streamflow volumes and hydrograph shapes than the other routing configurations when reservoirs exist in the watershed. Additionally, the AQPI X-band radar rainfall estimates (without gauge correction) provide reasonable streamflow simulations, and they show better performance in reproducing the hydrograph peaks compared with the gauge-corrected rainfall estimates based on the operational S-band Next Generation Weather Radar network. Also, a sensitivity test reveals that surficial conditions have a significant influence on the streamflow simulation during the storm: the discharge increases to a higher level as the infiltration factor (REFKDT) decreases, and its peak goes down and lags as surface roughness coefficient (Mann) increases. The time delay analysis of precipitation input on the streamflow at the two outfalls of the surveyed watersheds further demonstrates the link between AQPI gap-filling radar observations and streamflow changes in this urban region.

© 2021 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: Y. Ma, yingzhao.ma@colostate.edu

This article is included in the 12th International Precipitation Conference (IPC12) Special Collection.

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