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Quantitative evaluation of NEXRAD data and its application to the distributed hydrologic model BPCC

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Abstract

The next-generation weather radar (NEXRAD) can generally capture the spatial variability of rainfall fields, but fails to provide accurate depth measurements. A systematic strategy to evaluate the accuracy of radar data in depth measurement and its performance in hydrologic model is outlined. Statistical evaluation coefficients are calculated by comparing NEXRAD data with individual raingauges as well as subbasin-averaged interpolations, and point- and surface-average factors are introduced to revise radar data successively. Hydrologic simulations are then performed with a distributed hydrologic model, called basin pollution calculation center (BPCC) with both raingauge observations and revised NEXRAD estimates inputs. The BPCC model is applied to Clear Creek Watershed, IA, USA, on an hourly scale, and the calibration and validation parameters are semi-automatically optimized to improve manual calibration shortcomings. Results show that hydrographs generated from both gauge and NEXRAD are in good agreement with observed flow hydrographs. Coefficient statistics reveal that NEXRAD contributes to model performance, indicating that NEXRAD data has the potential to be used as an alternative source of precipitation data and improve the accuracy of hydrologic simulations.

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References

  1. Liu Z Y, Tan B Q, Tao X, et al. Application of a distributed hydrologic model to flood forecasting in catchments of different conditions. J Hydrol Eng, 2008, 13(5): 378–384

    Article  Google Scholar 

  2. Getirana A C V, Bonnet M P, Calmant S, et al. Hydrologic monitoring of poorly gauged basins based on rainfall-runoff modeling and spatial altimetry. J Hydrol, 2009, 379: 205–219

    Article  Google Scholar 

  3. Sharif H O, Sparks L, Hassan A A, et al. Application of a Distributed Hydrologic Model to the November 17, 2004, Flood of Bull Creek Watershed, Austin, Texas. J Hydrol Eng, 2010, 15(8): 651–657

    Article  Google Scholar 

  4. Zheng F, Zimmer A, Bedient P B, et al. Using a Distributed Hydrologic Model to Evaluate the Location of Urban Development and Flood Control Storage. J Water Res Pl-ASCE, 2010, 136(5): 597–601

    Article  Google Scholar 

  5. Wang Z G, Zheng H X, Liu C M, et al. A distributed hydrologic model with its application to the Jinghe watershed in the Yellow River Basin. Sci China Set E-Tech Sci, 2004, 47: 60–71

    Article  Google Scholar 

  6. McMillan H, Jackson B, Clark M, et al. Rainfall uncertainty in hydrologic modelling: An evaluation of multiplicative error models. J Hydrol, 2011, 400: 83–94

    Article  Google Scholar 

  7. Kalin L, Hantush M M. Hydrologic modeling of an eastern pennsylvania watershed with NEXRAD and rain gauge data. J Hydrol Eng, 2006, 11(6): 555–569

    Article  Google Scholar 

  8. Collier C G. Applications of Weather Radar Systems: A Guide to Uses of Radar Data in Meteorology. Chichester: Ellis Horwood, 1989

    Google Scholar 

  9. Krajewski W F, Smith J A. Radar hydrology: rainfall estimation. Adv Water Res, 2002, 25: 1387–1394

    Article  Google Scholar 

  10. Ciach G J, Crajewski W F, Gabriele V. Product-error-driven uncertainty model for probabilistic quantitative precipitation estimation with NEXRAD data. J Hydrometeor, 2007, 8(6): 1325–1347

    Article  Google Scholar 

  11. Kalinga O A, Gan T Y. Semi-distributed modeling of basin hydrology with radar and gauged precipitation. Hydrol Process, 2006, 20: 3725–3746

    Article  Google Scholar 

  12. Shi R, Cheng M H, Cui Z H, et al. Estimation amount of summer regional heavy rainfall using the averages of radar VPRs together with rain gauge adjustment (in Chinese). J Appl Meteor Sci, 2005, 16(6): 737–745

    Google Scholar 

  13. Li J T, Yang W S, Guo L, et al. A study of improving precision of measuring regional precipitation in optimum interpolation method (in Chinese). Chin J Atmos Sci, 2000, 24(2): 263–270

    Google Scholar 

  14. Xie H, Zhou X, Vivoni E R, Hendrickx J M H, et al. GIS-based NEXRAD Stage III precipitation database: automated approaches for data processing and visualization. Comput Geosci-UK, 2005, 31: 65–76

    Article  Google Scholar 

  15. Hardegree S P, Van Vactor S S, Levinson D H, et al. Evaluation of NEXRAD radar precipitation products for natural resource applications. Rangeland Ecol Manag, 2008, 61: 346–353

    Article  Google Scholar 

  16. Krajewski W F, Kruger A, Smith J A, et al. Towards better utilization of NEXRAD data in hydrology: an overview of Hydro-NEXRAD. World Environmental and Water Resources Congress: Restoring Our Natural Habitat, 2007

  17. Burlando P. Multi-sensor precipitation measurements integration, calibration and flood forecasting (MUSIC), 2001. http://www.geomin.unibo.it/hydro/music/index2.htm

  18. Bedient P B, Hoblit B C, Gladwell D C, et al. NEXRAD radar for flood prediction in Houston. J Hydrol Eng, 2000, 5(3): 269–277

    Article  Google Scholar 

  19. Bedient P B, Anthony H, Benavides J A, et al. Radar-based flood warning system applied to Tropical Storm Allison. J Hydrol Eng, 2003, 8(6): 308–318

    Article  Google Scholar 

  20. Jayakrishnan R, Srinivasan R, Santhi C, et al. Advances in the application of the SWAT model for water resources management. Hydrol Process, 2005, 19: 749–762

    Article  Google Scholar 

  21. Luzioa M D, Arnold J G. Formulation of a hybird calibration approach for a physically based didtributed model with NEXRAD data input. J Hydrol, 2004, 298: 136–154

    Article  Google Scholar 

  22. Neary V S, Asce M, Habib E, et al. Hydrologic modeling with NEXRAD precipitation in middle Tennessee. J Hydrol Eng, 2004, 9(5): 339–349

    Article  Google Scholar 

  23. Zhang H L, Li D X, Wang X K. Distributed hydrologic and sediment erosion model with its application to Zhengjiangguan watershed in Minjiang river basin (in Chinese). J Hydrodyn, 2011, 26(1): 1–10

    Google Scholar 

  24. Xie H, Zhou J, Hendrickx E, et al. Comparison of NEXRAD stage III and gauge precipitation estimates in central New Mexico. J Am Water Resour As, 2006, 42: 237–256

    Article  Google Scholar 

  25. Young C B, Brunsell N A. Evaluating NEXRAD estimates for the Missouri River Basin: analysis using daily raingauge data. J Hydrol Eng, 2008, 13: 549–553

    Article  Google Scholar 

  26. Zhang X S, Srinivasan R. GIS-based spatial precipitation estimation using next generation radar and raingauge data. Environ Modell Softw, 2010, 25: 1781–1788

    Article  Google Scholar 

  27. Wei Y, Chen M Q, Li Y. A research on improving the weighted method with direction distance (in Chinese). Acta Petrol Sinica, 1998, 4: 89–93

    Google Scholar 

  28. Xu J J, Cai Z G, Liu Z W, et al. Flood forecasting in the Three-Gorge reach based on a distributed hydrologic model (II): application of radar rainfall data (in Chinese). J China Hydrol, 2008, 28(2): 18–22

    Google Scholar 

  29. Zhang C. Distributed Non-point Sources Pollution Modeling and Its Application in Xiangxi Watershed (in Chinese). Dissertation of Doctoral Degree. Beijing: Tsinghua University, 2008

    Google Scholar 

  30. Li W J. Research and Application of Physically Based Water Erosion and Sediment Yield Model (in Chinese). Dissertation of Doctoral Degree. Beijing: Tsinghua University, 2011

    Google Scholar 

  31. Zhang H L. A Distributed Hydrologic Model Coupled with Soil Erosion and Its Application in River Basins (in Chinese). Dissertation of Doctoral Degree. Beijing: Tsinghua University, 2011

    Google Scholar 

  32. Todini E, Ferraresi M. Influence of parameter estimation uncertainty in Kriging. J Hydrol, 1996, 175: 555–566

    Article  Google Scholar 

  33. Allard D. Geostatistical classification and class Kriging. J Geograp Inform Decis Analys (GIDA), 1998, 2(2): 77–90

    MathSciNet  Google Scholar 

  34. Nalder I A, Wein R W. Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agr Forest Meteorol, 1998, 92: 211–225

    Article  Google Scholar 

  35. Horsburgh J S, Tsarboton D G. ODM data loader functional specifications. CUAHSI HIS online report, 2008, http://his.cuahsi.org/odmdatabases.html

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Authors and Affiliations

  1. State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, 100084, China

    HuiLan Zhang, DanXun Li, XingKui Wang & XiaoMing Jiang

Authors
  1. HuiLan Zhang
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  2. DanXun Li
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  3. XingKui Wang
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  4. XiaoMing Jiang
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Correspondence to XingKui Wang.

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Zhang, H., Li, D., Wang, X. et al. Quantitative evaluation of NEXRAD data and its application to the distributed hydrologic model BPCC. Sci. China Technol. Sci. 55, 2617–2624 (2012). https://doi.org/10.1007/s11431-012-4918-2

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  • DOI: https://doi.org/10.1007/s11431-012-4918-2

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