Paddy and Water Environment

, Volume 9, Issue 3, pp 275–290

A grid-based rainfall-runoff model for flood simulation including paddy fields

  • In-Kyun Jung
  • Jong-Yoon Park
  • Geun-Ae Park
  • Mi-Seon Lee
  • Seong-Joon Kim
Article

Abstract

A grid-based, KIneMatic wave STOrm Runoff Model (KIMSTORM) is described. The model adopts the single flow-path algorithm and routes the water balance during the storm period. Manning’s roughness coefficient adjustment function of the paddy cell was applied to simulate the flood mitigation effect of the paddy fields for the grid-based, distributed rainfall-runoff modeling. The model was tested in 2296 km2 dam watershed in South Korea using six typhoon storm events occurring between 2000 and 2007 with 500 m spatial resolution, and the results were tested through the automatic model evaluation functions in the model. The average values of the Nash–Sutcliffe model efficiency (ME), the volume conservation index (VCI), the relative error of peak runoff rate (EQp), and the absolute error of peak runoff (ETp) were 0.974, 1.016, 0.019, and 0.45 h for calibrated storm events and 0.975, 0.951, 0.029, and 0.50 h for verified storm events, respectively. In the simulation of the flood mitigation effect of the paddy fields, the average values of the percentage changes for peak runoff, total runoff volume, and time to peak runoff were only −1.95, −0.93, and 0.19%, respectively.

Keywords

Distributed hydrological model Grid-based water balance Paddy field Flood mitigation function of paddies Visualization of storm event 

References

  1. Abbott MB, Bathurst JC, Cunge JA, O’Connell PE, Rasmussen J (1986a) An introduction to the European Hydrological System—Systeme Hydrologique Europeen, ‘SHE’, 1: history and philosophy of a physically-based, distributed modeling system. J Hydrol 87:45–59CrossRefGoogle Scholar
  2. Abbott MB, Bathurst JC, Cunge JA, O’Connell PE, Rasmussen J (1986b) An introduction to the European Hydrological System—Systeme Hydrologique Europeen, ‘SHE’, 2: structure of a physically-based, distributed modeling system. J Hydrol 87:61–77CrossRefGoogle Scholar
  3. Beasley DB, Huggins LF, Monke EJ (1980) ANSWERS: a model for watershed planning. Trans ASAE 23(4):938–944Google Scholar
  4. Bell VA, Kay AL, Jones RG, Moore RJ (2007) Development of a high resolution grid-based river flow model for use with regional climate model output. Hydrol Earth Syst Sci 11:532–549CrossRefGoogle Scholar
  5. Beven KJ (1982) On subsurface stormflow: predictions with simple kinematic theory for saturated and unsaturated flows. Water Resour Res 18:1627–1633CrossRefGoogle Scholar
  6. Beven KJ (1996) A discussion of distributed modeling, chap 13A. In: Refsgaard JC, Abbott MB (eds) Distributed hydrological modelling. Kluwer, Dordrecht, pp 255–278Google Scholar
  7. Beven KJ, Kirkby MJ (1979) A physically based variable contributing area model of basin hydrology. Hydrol Sci Bull 24(1):43–69CrossRefGoogle Scholar
  8. Chow VT, Maidment DR, Mays LW (eds) (1988) Applied hydrology. McGraw-Hill, New York, pp 20–52Google Scholar
  9. Chung HW, Kim SJ, Choi JY, Kim DS (1995) GIS application model for temporal and spatial simulation of surface runoff from a small watershed. GIS Assoc Korea 3(2):135–146Google Scholar
  10. Downer CW, Ogden FL (2003) Gridded surface subsurface hydrologic analysis user’s manual. U.S. Army Engineer Research and Development Center, VicksburgGoogle Scholar
  11. Grayson RB, Moore ID, McMahon TA (1992) Physically based hydrological modeling: 1. A terrain-based model for investigative purposes. Water Resour Res 28(10):2639–2658CrossRefGoogle Scholar
  12. Grayson RB, Blöschl G, Moore ID (1995) Distributed parameter hydrologic modeling using vector elevation data: THALES and TAPES-C. In: Singh VP (ed) Computer models of watershed hydrology. Water Resources Publications, Highlands Ranch, pp 669–696Google Scholar
  13. Griensven A, Meixner T, Grunwald S, Bishop T, Diluzio M, Srinivasan R (2006) A global sensitivity analysis tool for the parameters of multi-variable catchment models. J Hydrol 324:10–23CrossRefGoogle Scholar
  14. Hellweger FL (1997) AGREE-DEM surface reconditioning system. University of Texas. http://www.ce.utexas.edu/prof/maidment/gishydro/ferdi/research/agree/agree.html. Accessed 3 April 2003
  15. Huggins LF, Monke EJ (1968) A mathematical model for simulating the hydrologic response of a watershed. Water Resour Res 4(3):529–539CrossRefGoogle Scholar
  16. Jain MK, Kothyari UC, Ranga Raju KG (2004) A GIS based distributed rainfall-runoff model. J Hydrol 299:107–135CrossRefGoogle Scholar
  17. Jinkang D, Shunping X, Youpeng X, Chong-yu X, Singh VP (2007) Development and testing of a simple physically-based distributed rainfall-runoff model for storm runoff simulation in humid forested basins. J Hydrol 336:334–346CrossRefGoogle Scholar
  18. Julien PY, Saghafian B, Ogden FL (1995) Raster based hydrological modeling of spatially-varied surface runoff. Water Resour Bull AWRA 31(3):523–536CrossRefGoogle Scholar
  19. Jung IK, Kim SJ (2003) Comparison of DEM preprocessing method for efficient watershed and stream network extraction. KSCE J Civ Eng 23(3):393–400Google Scholar
  20. Jung IK, Lee MS, Park JY, Kim SJ (2008) A Modified grid-based KIneMatic wave STOrm Runoff Model (ModKIMSTORM), I. Theory and model. KSCE J Civ Eng 28(6):697–707Google Scholar
  21. Kim SJ (1998) Grid-based KIneMatic Wave STOrm Runoff Model (KIMSTORM), I. Theory and model. J Korea Water Resour Assoc 30(3):303–308Google Scholar
  22. Kim SJ (2001) GIS-based water resources management information system (WAMIS) in Korea. Korean J Limnol 34(4):349–356Google Scholar
  23. Kim SJ, Steenhuis TS (2001) GRISTORM: grid-based variable source area storm runoff model. Trans ASAE 44(4):863–875Google Scholar
  24. Kim SJ, Chae HS, Shin SC (1998) Grid-based KIneMatic Wave STOrm Runoff Model (KIMSTORM), II. Application—applied to Yoncheon Dam watershed. J Korea Water Resour Assoc 30(3):309–316Google Scholar
  25. Kim SJ, Kwon HJ, Jung IK, Park GA (2003) A comparative study on grid-based storm runoff prediction using Thiessen and spatially distributed rainfall. Paddy Water Environ 1(3):149–155CrossRefGoogle Scholar
  26. King KW, Arnold JG, Bingner RL (1999) Comparison of Green-Ampt and curve number methods on Goodwin creek watershed using SWAT. Trans ASAE 42(4):919–925Google Scholar
  27. Kojiri T, Tokai A, Kinai Y (1998) Assessment of river basin environment though simulation with water quality and quantity. Annu Disaster Prev Res Inst Kyoto Univ 41(B2):119–134Google Scholar
  28. Luo Q (2007) A distributed surface flow model for watersheds with large water bodies and channel loops. J Hydrol 337:172–186CrossRefGoogle Scholar
  29. Mein RG, Larson CL (1973) Modeling infiltration during a steady rain. Water Resour Res 9(2):384–394CrossRefGoogle Scholar
  30. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models, Part I—a discussion of principles. J Hydrol 10:283–290Google Scholar
  31. Ogden FL (1998) CASC2D version 1.18 user’s manual. Department of Civil and Environmental Engineering U-37, University of Connecticut, StorrsGoogle Scholar
  32. Rawls WJ, Brakensiek DL, Saxton KE (1982) Estimation of soil water properties. Trans ASAE 25:1316–1320, 1328Google Scholar
  33. Refsgaard JC (1997) Parameterisation, calibration and verification of distributed hydrological models. J Hydrol 198:69–97CrossRefGoogle Scholar
  34. Refsgaard JC, Storm B (1995) MIKE SHE. In: Singh VP (ed) Computer models of watershed hydrology. Water Resources Publications, Highlands Ranch, pp 809–846Google Scholar
  35. Shepard D (1968) A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings of the 1968 ACM national conference, pp 517–524Google Scholar
  36. Sloan PG, Moore ID (1984) Modeling subsurface stormflow on steeply sloping forested watersheds. Water Resour Res 20(12):1815–1822CrossRefGoogle Scholar
  37. Takeuchi J, Kawachi T, Unami K, Maeda S, Izumi T (2009) A distributed hydro-environmental watershed model with three-zoned cell profiling. Paddy Water Environ 7:33–43CrossRefGoogle Scholar
  38. Vieux BE (2004) Distributed hydrologic modeling using GIS, 2nd edn. Kluwer Academic Publishers, The Netherlands, pp 91–128Google Scholar
  39. Vieux BE, Vieux JE (2002) Vflo TM: a real-time distributed hydrologic model. In: Proceedings of the second federal interagency hydrologic modeling conference, 28 July–1 Aug 2002, Las VegasGoogle Scholar
  40. Young RA, Onstad CA, Bosch DD, Anderson WP (1989) AGNPS: a nonpoint-source pollution model for evaluating agricultural watersheds. J Soil Water Conserv 44(2):168–173Google Scholar
  41. Young RA, Onstad CA, Bosch DD (1995) AGNPS: an agricultural non-point source model. In: Singh VP (ed) Computer models of watershed hydrology. Water Resources Publications, Highlands Ranch, pp 1001–1020Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • In-Kyun Jung
    • 1
  • Jong-Yoon Park
    • 1
  • Geun-Ae Park
    • 1
  • Mi-Seon Lee
    • 1
  • Seong-Joon Kim
    • 1
  1. 1.Department of Civil and Environmental System EngineeringKonkuk UniversitySeoulSouth Korea

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