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WRF-Hydro Model Application in a Data-Scarce, Small and Topographically Steep Catchment in Samsun, Turkey

  • Research Article - Civil Engineering
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Abstract

Floods due to heavy rainfall are one of the most frequent and widespread natural hazards. Rainfall is one of the key variables in flood modeling. For topographically steep catchments, flood modeling requires accurate rainfall sources in both time and space. The objective of this study is to compare different rainfall sources in physics-based distributed hydrologic model, (Weather Research and Forecasting) WRF-Hydro, in a data-scarce, small and topographically steep catchment. For this purpose, the model was calibrated and validated for the three catastrophic flood events that occurred in the Terme basin of eastern Black Sea region in Samsun, Turkey. The rainfall datasets include weather radar data and the Hydro-Estimator satellite rainfall product as nowcasting products, and WRF model precipitation data as a forecasting product and gauge-based data. Our results indicated that the tested rainfall products have different limitations and potentials depending on the rainfall process, so the accuracy of the results is greatly affected by the accuracy of rainfall products. Among the flood hydrographs, WRF precipitation data, bias-adjusted radar data and gauge data gave best Nash–Sutcliffe efficiency (NSE) results with calibrated parameters in simulations belonging to floods observed on November 22, 2014, August 2, 2015, and May, 28, 2016, respectively.

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References

  1. Nyeko, M.: Hydrologic modelling of data scarce basin with SWAT model: capabilities and limitations. Water Resour. Manage 29, 81–94 (2015). https://doi.org/10.1007/s11269-014-0828-3

    Article  Google Scholar 

  2. Bangira T (2013) Mapping of flash flood potential areas in the Western Kape, South Africa, using remote sensing and in situ data. Master Thesis, University of Twente Faculty of Geo-Information and Earth Observation (ITC)

  3. Bitew, M.M.; Gebremichael, M.: Assessment of satellite rainfall products for streamflow simulation in medium watersheds of the Ethiopian highlands. Hydrol. Earth Syst. Sci. 15, 1147–1155 (2011)

    Article  Google Scholar 

  4. Vasiloff, S.V.; Seo, D.-J.; Howard, K.W.; et al.: Improving QPE and very short term QPF: an initiative for a community-wide integrated approach. Bull. Am. Meteorol. Soc. 88, 1899–1911 (2007)

    Article  Google Scholar 

  5. Habets, F.; LeMoigne, P.; Noilhan, J.: On the utility of operational precipitation forecasts to served as input for streamflow forecasting. J. Hydrol. 293, 270–288 (2004)

    Article  Google Scholar 

  6. Petty, G.W.; Krajewski, W.F.: Satellite estimation of precipitation over land. Hydrol. Sci. J. 41, 433–451 (1996). https://doi.org/10.1080/02626669609491519

    Article  Google Scholar 

  7. Sapiano, M.R.P.; Arkin, P.A.: An intercomparison and validation of high-resolution satellite precipitation estimates with 3-hourly gauge data. J. Hydrometeorol. 10, 149–166 (2009). https://doi.org/10.1175/2008JHM1052.1

    Article  Google Scholar 

  8. Nikolopoulos, E.I.; Anagnostou, E.N.; Borga, M.: Using high-resolution satellite rainfall products to simulate a major flash flood event in Northern Italy. J. Hydrometeorol. 14, 171–185 (2013). https://doi.org/10.1175/JHM-D-12-09.1

    Article  Google Scholar 

  9. Gourley, J.J.; Hong, Y.; Flamig, Z.L.; et al.: Hydrologic evaluation of rainfall estimates from radar, satellite, gauge, and combinations on Ft. Cobb basin, Oklahoma. J. Hydrometeorol. 12, 973–988 (2011). https://doi.org/10.1175/2011JHM1287.1

    Article  Google Scholar 

  10. Mei, Y.; Nikolopoulos, E.I.; Anagnostou, E.N.; et al.: Error analysis of satellite precipitation-driven modeling of flood events in complex alpine terrain. Remote Sens. 8, 293 (2016). https://doi.org/10.3390/rs8040293

    Article  Google Scholar 

  11. Nikolopoulos, E.I.; Anagnostou, E.N.; Hossain, F.; et al.: Understanding the scale relationships of uncertainty propagation of satellite rainfall through a distributed hydrologic model. J. Hydrometeorol. 11, 520–532 (2010). https://doi.org/10.1175/2009JHM1169.1

    Article  Google Scholar 

  12. Zahidul, Islam; Yew, Gan Thian: Hydrologic modeling of the blue river basin using NEXRAD precipitation data with a semidistributed and a fully distributed model. J. Hydrol. Eng. 20, 04015015 (2015). https://doi.org/10.1061/(ASCE)HE.1943-5584.0001179

    Article  Google Scholar 

  13. Vieux, B.E.; Bedient, P.B.: Estimation of rainfall for flood prediction from WSR-88D reflectivity: a case study, 17–18 October 1994. Weather Forecast. 13, 407–415 (1998). https://doi.org/10.1175/1520-0434(1998)013%3c0407:EORFFP%3e2.0.CO;2

    Article  Google Scholar 

  14. Kalinga, O.A.; Gan, T.Y.: Semi-distributed modelling of basin hydrology with radar and gauged precipitation. Hydrol. Process. 20, 3725–3746 (2006). https://doi.org/10.1002/hyp.6385

    Article  Google Scholar 

  15. Javelle, P.; Demargne, J.; Defrance, D.; et al.: Evaluating flash-flood warnings at ungauged locations using post-event surveys: a case study with the AIGA warning system. Hydrol. Sci. J. 59, 1390–1402 (2014). https://doi.org/10.1080/02626667.2014.923970

    Article  Google Scholar 

  16. Li, Y.; Grimaldi, S.; Walker, J.P.; Pauwels, V.R.N.: Application of remote sensing data to constrain operational rainfall-driven flood forecasting: a review. Remote Sens. 8, 456 (2016). https://doi.org/10.3390/rs8060456

    Article  Google Scholar 

  17. Shrestha, D.L.; Robertson, D.E.; Wang, Q.J.; et al.: Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose. Hydrol. Earth Syst. Sci. 17, 1913–1931 (2013). https://doi.org/10.5194/hess-17-1913-2013

    Article  Google Scholar 

  18. Lorenz, E.N.: The predictability of a flow which possesses many scales of motion. Tellus 21, 289–307 (1969). https://doi.org/10.1111/j.2153-3490.1969.tb00444.x

    Article  Google Scholar 

  19. Rogelis, M.C.; Werner, M.: Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas. Hydrol. Earth Syst. Sci. 22, 853–870 (2018)

    Article  Google Scholar 

  20. Wu, X.: Quarterly numerical weather prediction model performance summary October to December 2009. Aust. Meteorol. Oceanogr. J. 60, 87–90 (2010)

    Article  Google Scholar 

  21. Chintalapudi, S.; Sharif, H.O.; Furl, C.: High-resolution, fully distributed hydrologic event-based simulations over a large watershed in Texas. Arab. J. Sci. Eng. 42, 1341–1357 (2017). https://doi.org/10.1007/s13369-017-2446-x

    Article  Google Scholar 

  22. Liang, X.; Lettenmaier, D.P.; Wood, E.F.; Burges, S.J.: A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. Atmos. 99, 14415–14428 (1994)

    Article  Google Scholar 

  23. Arnold, J.G.; Atwood, J.D.; Benson, V.W.; et al.: Potential Environmental and Economic Impacts of Implementing National Conservation Buffer Initiative Sedimentation Control Measures. USDA, NRCS Staff Paper (1998)

  24. Beven, K.J.; Kirkby, M.J.: A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol. Sci. Bull. 24, 43–69 (1979). https://doi.org/10.1080/02626667909491834

    Article  Google Scholar 

  25. Devia, G.K.; Ganasri, B.P.; Dwarakish, G.S.: A review on hydrological models. Aquat. Procedia 4, 1001–1007 (2015)

    Article  Google Scholar 

  26. Maidment, D.R.: Conceptual framework for the national flood interoperability experiment. JAWRA J. Am. Water Resour. Assoc. 53, 245–257 (2017)

    Article  Google Scholar 

  27. Gochis, D.J.; Yu, W.; Yates, D.N.: The WRF-Hydro Model Technical Description and User’s Guide, Version 3.0, NCAR Technical Document (2015)

  28. Yucel, I.; Onen, A.; Yilmaz, K.K.; Gochis, D.J.: Calibration and evaluation of a flood forecasting system: utility of numerical weather prediction model, data assimilation and satellite-based rainfall. J. Hydrol. 523, 49–66 (2015)

    Article  Google Scholar 

  29. Senatore, A.; Mendicino, G.; Gochis, D.J.; et al.: Fully 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, 1693–1715 (2015)

    Article  Google Scholar 

  30. Arnault, J.; Wagner, S.; Rummler, T.; et al.: 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. Hydrometeorol. 17, 1489–1516 (2015). https://doi.org/10.1175/JHM-D-15-0089.1

    Article  Google Scholar 

  31. Kerandi, N.; Arnault, J.; Laux, P.; et al.: Joint atmospheric-terrestrial water balances for East Africa: a WRF-Hydro case study for the upper Tana River basin. Theor. Appl. Climatol. 131, 1337–1355 (2018)

    Article  Google Scholar 

  32. Silver, M.; Karnieli, A.; Ginat, H.; et al.: An innovative method for determining hydrological calibration parameters for the WRF-Hydro model in arid regions. Environ. Model. Softw. 91, 47–69 (2017)

    Article  Google Scholar 

  33. Sensoy, S.: The mountains influence on Turkey climate. Paper presented at the BALWOIS conference on water observation and information system for decision support, Macedonia, May 25–29 (2010).

  34. Oyj, V.: Weather Radar Documentation User Guide IRIS Radar (2017)

  35. Marshall, J.S.; Palmer, W.M.K.: The distribution of raindrops with size. J. Meteorol. 5, 165–166 (1948)

    Article  Google Scholar 

  36. Ozkaya, A.: Assessment of different rainfall products in flood simulations. Ph.D. Thesis, Middle East Technical University (2017)

  37. Ozkaya, A.; Akyurek, Z.: Evaluating the use of bias-corrected radar rainfall data in three flood events in Samsun, Turkey. Nat. Hazards 98, 643–674 (2019). https://doi.org/10.1007/s11069-019-03723-z

    Article  Google Scholar 

  38. Scofield, R.A.; Kuligowski, R.J.: Status and outlook of operational satellite precipitation algorithms for extreme-precipitation events. Weather Forecast. 18, 1037–1051 (2003)

    Article  Google Scholar 

  39. Vicente, G.A.; Scofield, R.A.; Menzel, W.P.: The operational GOES infrared rainfall estimation technique. Bull. Am. Meteorol. Soc. 79, 1883–1898 (1998). https://doi.org/10.1175/1520-0477(1998)079%3c1883:TOGIRE%3e2.0.CO;2

    Article  Google Scholar 

  40. NOAA’s Office of Satellite and Product Operations. In: Global Hydro-Estimator—Algorithm Description. https://www.ospo.noaa.gov/Products/atmosphere/ghe/algo.html. Accessed 10 Sept 2018

  41. Yucel, I.: Assessment of a flash flood event using different precipitation datasets. Nat. Hazards 79, 1889–1911 (2015)

    Article  Google Scholar 

  42. Skamarock, W.C.; Klemp, J.B.; Dudhia, J.: Prototypes for the WRF (Weather Research and Forecasting) model. In: Preprints, Ninth Conference Mesoscale Processes, J11–J15, Am. Meteorol. Soc., Fort Lauderdale, FL (2001)

  43. Yucel, I.; Onen, A.: Evaluating a mesoscale atmosphere model and a satellite-based algorithm in estimating extreme rainfall events in northwestern Turkey. Nat. Hazards Earth Syst. Sci. 14, 611–624 (2014)

    Article  Google Scholar 

  44. Chen, C.-S.; Lin, Y.-L.; Peng, W.-C.; Liu, C.-L.: Investigation of a heavy rainfall event over southwestern Taiwan associated with a subsynoptic cyclone during the 2003 Mei-Yu season. Atmos. Res. 95, 235–254 (2010)

    Article  Google Scholar 

  45. Liu, J.; Bray, M.; Han, D.: A study on WRF radar data assimilation for hydrological rainfall prediction. Hydrol. Earth Syst. Sci. 17, 3095–3110 (2013)

    Article  Google Scholar 

  46. Hong, S.-Y.; Dudhia, J.; Chen, S.-H.: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Weather Rev. 132, 103–120 (2004)

    Article  Google Scholar 

  47. Kain, J.S.: The Kain-Fritsch convective parameterization: an update. J. Appl. Meteorol. 43, 170–181 (2004)

    Article  Google Scholar 

  48. Que, L.-J.; Que, W.-L.; Feng, J.-M.: Intercomparison of different physics schemes in the WRF model over the Asian summer monsoon region. Atmos. Ocean. Sci. Lett. 9, 169–177 (2016). https://doi.org/10.1080/16742834.2016.1158618

    Article  Google Scholar 

  49. Wöhling, T.; Samaniego, L.; Kumar, R.: Evaluating multiple performance criteria to calibrate the distributed hydrological model of the upper Neckar catchment. Environ. Earth Sci. 69, 453–468 (2013)

    Article  Google Scholar 

  50. Nash, J.E.; Sutcliffe, J.V.: River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol. 10, 282–290 (1970)

    Article  Google Scholar 

  51. Mitchell, K.E.: The Community Noah Land Surface Model (LSM)—user’s guide (v2. 2) (2001)

  52. Cencetti, C.; Rosa, P.D.; Fredduzzi, A.; et al.: A statistical test for drainage network recognition using MeanStreamDrop analysis. Geomat. Nat. Hazards Risk 6, 534–553 (2015)

    Article  Google Scholar 

  53. Frissell, C.A.; Liss, W.J.; Warren, C.E.; Hurley, M.D.: A hierarchical framework for stream habitat classification: viewing streams in a watershed context. Environ. Manag. 10, 199–214 (1986)

    Article  Google Scholar 

  54. Tarboton, D.G.; Bras, R.L.; Rodriguez-Iturbe, I.: On the extraction of channel networks from digital elevation data. Hydrol. Process. 5, 81–100 (1991)

    Article  Google Scholar 

  55. Seo, Y.; Schmidt, A.R.: Evaluation of drainage networks under moving storms utilizing the equivalent stationary storms. Nat. Hazards 70, 803–819 (2014)

    Article  Google Scholar 

  56. Özcan, E.: Sel Olayı ve Türkiye. Gazi Eğitim Fakültesi Dergisi 26, 35–50 (2006). https://doi.org/10.17152/gefd.15296

    Article  MathSciNet  Google Scholar 

  57. Huang, D.; Gao, S.: Impact of different cumulus convective parameterization schemes on the simulation of precipitation over China. Tellus A: Dyn. Meteorol. Oceanogr. 69, 1406264 (2017). https://doi.org/10.1080/16000870.2017.1406264

    Article  Google Scholar 

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Acknowledgements

Rainfall datasets including WRF, radar and gauge were provided by the General Directorate of Meteorology, and discharge data were provided by the State Hydraulic Works. A. Ozkaya acknowledges support of the Scientific and Technological Research Council of Turkey (TUBITAK) BIDEB 2211-A PhD scholarship program.

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Ozkaya, A., Akyurek, Z. WRF-Hydro Model Application in a Data-Scarce, Small and Topographically Steep Catchment in Samsun, Turkey. Arab J Sci Eng 45, 3781–3798 (2020). https://doi.org/10.1007/s13369-019-04251-5

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