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Uncertainty in Calibration of Variable Infiltration Capacity Model

  • Ankita Pradhan
  • J. InduEmail author
Chapter
Part of the Springer Water book series (SPWA)

Abstract

Hydrological models were developed to assess the impacts of climate change and to analyse, understand and examine solutions for sustainable water management in order to back the decision makers and hydrologists. It has been recognized that the current and future water challenges vary from the impacts of economic and population growth, floods, droughts or the melting of the glaciers. Hence, there is the need of the hour to adopt efficient hydrological models and understand the hydrological processes to provide sustainable solutions for water resources management. However, there are different sources of uncertainty associated with the hydrological simulation, such as inaccurate model input or input uncertainty, error associated with the model structure or the parametric uncertainty. The study examines a case study for the Mahanadi river basin of the Indian sub-continent to model uncertainty springing from calibration parameters of the Variable Infiltration Capacity (VIC) macroscale hydrological model. The VIC parameters are calibrated and validated at three gauging stations, namely, Tikarapara, Kantamal and Sundergarh for the years 2003–2007 and 2009–2011 respectively. It is demonstrated from the case study that the calibration parameters must be tuned well so as to match the observed discharge with the simulated discharge thereby eliminating or reducing the parametric uncertainty.

References

  1. 1.
    Abdulla FA (1996) Application of a macroscale hydrologic model to estimate the water balance of the Arkansas Red River basin. J Geophys Res Atmos 101(D3):7449–7459CrossRefGoogle Scholar
  2. 2.
    Abdulla FA, Lettenmaier DP (1997a) Development of regional parameter estimation equations for a macroscale hydrologic model. Journal of Hydrology 197(1–4):230–257Google Scholar
  3. 3.
    Abdulla FA, Lettenmaier DP (1997b) Application of regional parameter estimation schemes to simulate the water balance of a large continental river. Journal of Hydrology 197(1–4):258–285Google Scholar
  4. 4.
    Adam JC (2007) Understanding the causes of streamflow changes in the Eurasian Arctic. University of Washington, pp 156Google Scholar
  5. 5.
    Adam JC et al (2007) Simulation of reservoir influences on annual and seasonal streamflow changes for the Lena, Yenisei, and Ob’ rivers. J Geophys Res Atmos 112(D24)Google Scholar
  6. 6.
    Alley WM (1984) On the treatment of evapotranspiration, soil moisture accounting and aquifer, recharge in monthly water balance models, Water Resour Res 20(8):1137–1149Google Scholar
  7. 7.
    Anderson, J., Chung, F., Anderson, M., Brekke, L., Easton, D., Ejeta, M., Peterson, R., and Snyder, R (2007): Progress on incorporating climate change into management of California’s water resources, Climatic Change, 87, 91–108,  https://doi.org/10.1007/s10584-007-9353-1
  8. 8.
    Arsenault R, Brissette FP (2014) Continuous streamflow prediction in ungauged basins: the effects of equifinality and parameter set selection on uncertainty in regionalization approaches. Water Resour Res 50:6135–6153.  https://doi.org/10.1002/2013WR014898CrossRefGoogle Scholar
  9. 9.
    Bastola S, Murphy C, Sweeney J (2011) The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments. Adv Water Resour 34:562–576.  https://doi.org/10.1016/j.advwatres.2011.01.008CrossRefGoogle Scholar
  10. 10.
    Benke KK, Lowell KE, Hamilton AJ (2008) Parameter uncertainty, sensitivity analysis and prediction error in a waterbalance hydrological model. Math Comput Model 47:1134–1149.  https://doi.org/10.1016/j.mcm.2007.05.017CrossRefGoogle Scholar
  11. 11.
    Bennett KE, Werner AT, Schnorbus M (2012) Uncertainties in hydrologic and climate change impact analyses in headwater basins of British Columbia. J Climate 25:5711–5730.  https://doi.org/10.1175/jcli-d-11-00417.1
  12. 12.
    Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6:279–298.  https://doi.org/10.1002/hyp.3360060305CrossRefGoogle Scholar
  13. 13.
    Blondin C (1991) Parameterization of land-surface processes in numerical weather prediction. In: Schmugge TJ, Andre JC (eds) Land surface evaporation: measurements and parameterization. Springer, New York, pp 31–54CrossRefGoogle Scholar
  14. 14.
    Butts MB, Payne JT, Kristensen M, Madsen H (2004) An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation. J Hydrol 298:242–266.  https://doi.org/10.1016/j.jhydrol.2004.03.042CrossRefGoogle Scholar
  15. 15.
    Clark MP, Slater AG., Rupp DE, Woods RA, Vrugt JA,Gupta HV, Wagener T, Hay LE (2008) Framework for Understanding Structural Errors (FUSE): a modular framework to diagnose differences between hydrological models. Water Resour Res 44, W00B02.  https://doi.org/10.1029/2007wr006735
  16. 16.
    Clark MP, Kavetski D, Fenicia F (2011b) Pursuing the method of multiple working hypotheses for hydrological modeling. Water Resour Res 47:W09301.  https://doi.org/10.1029/2010wr009827
  17. 17.
    Clark MP, Nijssen B, Lundquist JD, Kavetski D, Rupp DE, Woods RA, Freer JE, Gutmann ED, Wood AW, Brekke LD, Arnold JR, Gochis DJ, Rasmussen RM (2015a) A unified approach for process-based hydrologic modeling: 1. Modeling concept. Water Resour Res 51:2498–2514.  https://doi.org/10.1002/2015wr017198
  18. 18.
    Clark MP, Nijssen B, Lundquist JD, Kavetski D, Rupp DE, Woods RA, Freer JE, Gutmann ED, Wood AW, Gochis DJ, Rasmussen RM, Tarboton DG. Mahat, V, Flerchinger GN, Marks DG (2015b) A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies, Water Resour Res 51:2515–2542.  https://doi.org/10.1002/2015wr017200
  19. 19.
    Deardorff JW (1978) Efficient prediction of ground surface-temperature and moisture, with inclusion of a layer of vegetation. J Geophys Res Ocean Atmos 83(NC4):1889–1903CrossRefGoogle Scholar
  20. 20.
    Dickinson RE (1984) Modeling evapotranspiration for three-dimentional global climate models. In: Hansen JE, Takahashi T (eds) Climate processes and Climate Sensitivity (Monograph Series). Washington, D.C., pp 58–72Google Scholar
  21. 21.
    Dickinson RE, Henderson-Sellers A, Kennedy PJ, Wilson MF (1986) Biosphere-atmosphertera nsfer scheme(BATS) for the NCAR community climate model. NCAR Tech. Note TN-275 +STRGoogle Scholar
  22. 22.
    Ducoudre NI et al (1993a) SECHIBA, a new set of parameterizations of the hydrologic exchanges at the land-atmosphere interface within the LMD atmospheric general circulation model. J Clim 6(2):248–273Google Scholar
  23. 23.
    Ducoudre NI et al (1993b) SECHIBA, a new set of parameterizations of the hydrologic exchanges at the land atmosphere interface within the LMD atmospheric general-circulation model. J Clim 6(2):248–273Google Scholar
  24. 24.
    Essery R, Morin S, Lejeune Y, Ménard CB (2013) A comparison of 1701 snow models using observations from an alpine site. Adv Water Resour 55:131–148.  https://doi.org/10.1016/j.advwatres.2012.07.013
  25. 25.
    Feld SI, Cristea NC, Lundquist JD (2013) Representing atmospheric moisture content along mountain slopes: examination using distributed sensors in the Sierra Nevada, California. Water Resour Res 49:4424–4441.  https://doi.org/10.1002/wrcr.20318CrossRefGoogle Scholar
  26. 26.
    Flint AL, Childs SW (1987) Calculation of solar radiation in mountainous terrain. Agr Forest Meteorol 40:233–249.  https://doi.org/10.1016/0168-1923(87)90061-XCrossRefGoogle Scholar
  27. 27.
    Franchini M, Pacciani M (1991) Comparative-analysis of several conceptual rainfall runoff models. J Hydrol 122(1–4):161–219CrossRefGoogle Scholar
  28. 28.
    Gabos A, Gasparri L (1983) Monthly runoff model for regional planning. Water Int 8:42–45Google Scholar
  29. 29.
    Gao H, Tang Q, Shi X, Zhu C, Bohn TJ, Su F, Sheffield J, Pan M, Lettenmaier DP, Wood EF (2010) Water budget record from variable infiltration capacity (VIC) model. In: Algorithm theoretical basis document for terrestrial water cycle data recordsGoogle Scholar
  30. 30.
    Georgakakos KP et al (2004) Towards the characterization of streamflow simulation uncertainty through multimodal ensembles. J Hydrol 298:222–241.  https://doi.org/10.1016/j.jhydrol.2004.03.037CrossRefGoogle Scholar
  31. 31.
    Guo JZ, Liang Xu, Leung LR (2004) Impacts of different precipitation data sources on water budgets. J Hydrol 298(1–4):311–334CrossRefGoogle Scholar
  32. 32.
    Herrero J, Polo MJ (2012) Parameterization of atmospheric longwave emissivity in a mountainous site for all sky conditions. Hydrol Earth Syst Sci 16:3139–3147.  https://doi.org/10.5194/hess-16-3139-2012
  33. 33.
    Hillard U, Sridhar V, Lettenmaier DP, McDonald KC (2003) Assessing snowmelt dynamics with NASA scatterometer (NSCAT) data and a hydrologic process model. Remote Sens Environ 86(1):52–69CrossRefGoogle Scholar
  34. 34.
    Huang M, Liang X, Liang Y (2003) A transferability study of model parameters for the variable infiltration capacity land surface scheme. J Geophys Res Atmos 108(D22)Google Scholar
  35. 35.
    Hurkmans R, Moel H, Aerts JCJH, Troch PA (2008) Water balance versus land surface model in the simulation of Rhine river discharges. Water Resour Res 44(1)Google Scholar
  36. 36.
    Jackson C, Xia Y, Sen MK, Stoffa PL (2003) Optimal parameter and uncertainty estimation of a land surface model: A case study using data from Cabauw, Netherlands. J Geophys Res 108:4583.  https://doi.org/10.1029/2002JD002991CrossRefGoogle Scholar
  37. 37.
    Jiang T, Chen YD, Xu C, Chen X, Chen X, Singh VP (2007) Comparison of hydrological impacts of climate change simulated by six hydrological models in the Dongjiang Basin, South China. J Hydrol 336:316–333.  https://doi.org/10.1016/j.jhydrol.2007.01.010CrossRefGoogle Scholar
  38. 38.
    Kelleher C, Wagener T, McGlynn B (2015) Model-based analysis of the influence of catchment properties on hydrologic partitioning across five mountain headwater subcatchments. Water Resour Res 51:1–28.  https://doi.org/10.1002/2014WR016147CrossRefGoogle Scholar
  39. 39.
    Kuczera G, Parent E (1998) Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm. J Hydrol 211:69–85.  https://doi.org/10.1016/S0022-1694(98)00198-XCrossRefGoogle Scholar
  40. 40.
    Lakshmi V, Wood EF (1998) Diurnal cycles of evaporation using a two-layer hydrological model. J Hydrol 204(1–4):37–51CrossRefGoogle Scholar
  41. 41.
    Liang X et al (2004) Assessment of the effects of spatial resolutions on daily water flux simulations. J Hydrol 298(1–4):287–310CrossRefGoogle Scholar
  42. 42.
    Liang X, Lettenmaier DP, Wood EF (1996) One-dimensional statistical dynamic representation of subgrid spatial variability of precipitation in the two-layer variable infiltration capacity model. J Geophys Res 101(D16):21403–21422CrossRefGoogle Scholar
  43. 43.
    Liang X, Lettenmaier DP, Wood EF, Burges SJ (1994) A Simple hydrologically Based Model of Land Surface Water and Energy Fluxes for GSMs. J Geophys Res 99(D7):14415–14428CrossRefGoogle Scholar
  44. 44.
    Liang X, Wood EF, Lettenmaier DP (1996) Surface soil moisture parameterization of the VIC-2L model: evaluation and modifications. Global Planet Change 13:195–206CrossRefGoogle Scholar
  45. 45.
    Liu Y, Gupta HV (2007) Uncertainty in hydrologic modeling: toward an integrated data assimilation framework. Water Resour Res 43:W07401.  https://doi.org/10.1029/2006WR005756CrossRefGoogle Scholar
  46. 46.
    Lohmann D, Nolte-Holube R, Raschke E (1996) A large-scale horizontal routing model to be coupled to land surface parametrization schemes. Tellus 48(A):708–721Google Scholar
  47. 47.
    Lohmann D, Raschke E, Nijssen B, Lettenmaier DP (1998) Regional scale hydrology: I. Formulation of the VIC-2L model coupled to a routing model. Hydrol Sci J 43(1):131–141Google Scholar
  48. 48.
    Manabe S (1969) Climate and the ocean circulation: I. The atmospheric circulation and the hydrology of the earth’s surface, Month Weather Rev 97(11):739–774Google Scholar
  49. 49.
    Matheussen B, Kirshbaum RL, Goodman IA, Donnel GM (2000) Effects of land cover change on streamflow in the interior Colunbia River Basin (USA and Canada). Hydrol, ProcessGoogle Scholar
  50. 50.
    Mote PW, Hamlet AF, Clark MP, Lettenmaier DP (2005) Declining mountain snowpack in western North America. B Am Meteorol Soc 86:39–49.  https://doi.org/10.1175/bams-86-1-39
  51. 51.
    Poulin A, Brissette F, Leconte R, Arsenault R, Malo JS (2011) Uncertainty of hydrological modelling in climate change impact studies in a Canadian, snow-dominated river basin. J Hydrol 409:626–636.  https://doi.org/10.1016/j.jhydrol.2011.08.057CrossRefGoogle Scholar
  52. 52.
    Refsgaard JC, van der Sluijs JP, Brown J, van der Keur P (2006) A framework for dealing with uncertainty due to model structure error. Adv Water Resour 29:1586–1597.  https://doi.org/10.1016/j.advwatres.2005.11.013
  53. 53.
    Sellers PJ, Mintz Y (1986) A Simple Biosphere Model (SiB) for use within generation circulation models. J Atmos. SciGoogle Scholar
  54. 54.
    Shuttleworth WJ (1993) Evaporation. In: Maidment DR (ed) Handbook of hydrology. McGraw-Hill, Inc., New York, pp 4.1–4.53Google Scholar
  55. 55.
    Slater AG, Schlosser CA, Desborough CE, Pitman AJ, Henderson-Sellers A, Robock A, Vinnikov KY, Entin J, Mitchell K, Chen F, Boone A, Etchevers P, Habets F, Noilhan J, Braden H, Cox PM, de Rosnay P, Dickinson RE, Yang Z-L, Dai Y-J, Zeng Q, Duan Q, Koren V, Schaake S, Gedney N, Gusev YM, Nasonova ON, Kim J, Kowalczyk EA, Shmakin AB, Smirnova TG, Verseghy D, Wetzel P, Xue Y (2001) The representation of snow in land surface schemes: results from PILPS 2(d). J Hydrometeorol 2:7–25. doi:10.1175/1525-7541(2001)002<0007:TROSIL>2.0.CO;2Google Scholar
  56. 56.
    Slater AG et al (2007) A multimodel simulation of pan-Arctic hydrology, J Geophys Res Biogeo, 112(G4)Google Scholar
  57. 57.
    Smith PJ, Beven KJ, Tawn JA (2008) Detection of structural inadequacy in process-based hydrological models: a particle-filtering approach. Water Resour Res 44:W01410.  https://doi.org/10.1029/2006WR005205CrossRefGoogle Scholar
  58. 58.
    Stamm JF et al (1994) Sensitivity of a GCM simulation of global climate to the representation of land-surface hydrology. J Clim 7(8):1218–1239CrossRefGoogle Scholar
  59. 59.
    Su FG, Xie ZH (2003) A model for assessing effects of climate change on runoff in China. Prog Nat Sci 13(9):701–707CrossRefGoogle Scholar
  60. 60.
    Tapiador FJ, Turk FJ, Petersen W, Hou AY, García-Ortega E, Machado LAT, Angelis CF, Salio P, Kidd C, Huffman GJ, de Castro M (2012) Global precipitation measurement: Methods, datasets and applications. Atmos Res 104–105:70–97.  https://doi.org/10.1016/j.atmosres.2011.10.021CrossRefGoogle Scholar
  61. 61.
    Teutschbein C, Wetterhall F, Seibert J (2011) Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale. Clim Dynam 37:2087–2105.  https://doi.org/10.1007/s00382-010-0979-8CrossRefGoogle Scholar
  62. 62.
    Tobin C, Nicotina L, Parlange MB, Berne A, Rinaldo A (2011) Improved interpolation of meteorological forcings for hydrologic applications in a Swiss Alpine region. J Hydrol 401:77–89.  https://doi.org/10.1016/j.jhydrol.2011.02.010CrossRefGoogle Scholar
  63. 63.
    Troy TJ et al (2008) An efficient calibration method for continental-scale land surface modeling. Water Resour Res 44(9):13CrossRefGoogle Scholar
  64. 64.
    Vandewiele GL, Xu, C-Y, Win NL (1992) Methodology and comparative study of monthly water balance models in Belgium, China and BurmaGoogle Scholar
  65. 65.
    Velazquez JA, Schmid J, Ricard S, Muerth MJ, Gauvin St-Denis B, Minville M, Chaumont D, Caya D, Ludwig R, Turcotte R (2013) An ensemble approach to assess hydrological models’ contribution to uncertainties in the analysis of climate change impact on water resources. Hydrol Earth Syst Sci 17:565–578.  https://doi.org/10.5194/hess-17-565-2013
  66. 66.
    Vrugt JA, Diks CGH, Gupta HV, Bouten W, Verstraten JM (2005) Improved treatment of uncertainty in hydrologic modeling: combining the strengths of global optimization and data assimilation. Water Resour Res 41:W01017.  https://doi.org/10.1029/2004WR003059CrossRefGoogle Scholar
  67. 67.
    Vrugt JA, Gupta HV, Bastidas LA, Bouten W, Sorooshian S (2003a) Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resour Res 39:1214.  https://doi.org/10.1029/2002wr001746
  68. 68.
    Vrugt JA, Gupta HV, Bouten W, Sorooshian S (2003b) A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour Res 39:1201.  https://doi.org/10.1029/2002wr001642
  69. 69.
    Wagener T, Gupta HV (2005) Model identification for hydrological forecasting under uncertainty. Stoch Environ Res Risk A 19:378–387.  https://doi.org/10.1007/s00477-005-0006-5CrossRefGoogle Scholar
  70. 70.
    Wilby RL, Harris I (2006) A framework for assessing uncertainties in climate change impacts: low-flow scenarios for the River Thames, UK. Water Resour Res 42:W02419.  https://doi.org/10.1029/2005WR004065CrossRefGoogle Scholar
  71. 71.
    Yuan F et al (2004) An application of the VIC-3L land surface model and remote sensing data in simulating streamflow for the Hanjiang River basin. Can J Remote Sens 30(5):680–690CrossRefGoogle Scholar
  72. 72.
    Zhao R-J et al (1980) The Xinanjiang model. In: Hydrological forecasting proceedings Oxford symposium, IASH 129, pp 351–356Google Scholar
  73. 73.
    Zhou S et al (2004) An assessment of the VIC-3L hydrological model for the Yangtze River basin based on remote sensing: a case study of the Baohe River basin. Can J Remote Sens 30(5):840–853CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Civil EngineeringIndian Institute of TechnologyBombayIndia

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