Bayesian parameter uncertainty modeling in a macroscale hydrologic model and its impact on Indian river basin hydrology under climate change

Water Resources Research

Volumn 48, Issue 8, 2012, Pages

  Raje, D ^a   Krishnan, R ^b  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

^b Center for Climate Change Research   (India)

Author keywords

[No Author keywords available]

Indexed keywords

A1B SCENARIOS; BAYESIAN APPROACHES; BAYESIAN STATISTICAL INFERENCE; CALIBRATION PARAMETERS; CHANGING CLIMATE; CLIMATIC VARIABLES; GEOGRAPHIC CHARACTERISTICS; HYDROLOGIC IMPACTS; HYDROLOGIC MODELS; HYDROLOGIC SIMULATIONS; HYDROLOGICAL SIMULATIONS; INDIAN RIVERS; LAND SURFACE HYDROLOGY; LARGE DOMAIN; LARGE RIVERS; MACRO SCALE; MACROSCALE MODELS; MARKOV CHAIN MONTE CARLO METHOD; MCMC APPROACH; METEOROLOGICAL VARIABLES; METROPOLIS-HASTINGS ALGORITHM; MODEL INPUTS; MODEL PARAMETERS; MODEL UNCERTAINTIES; NORTHEAST REGIONS; OBJECTIVE FUNCTIONS; PARAMETER UNCERTAINTY; POSTERIOR DISTRIBUTIONS; PREDICTIVE UNCERTAINTY; RIVER BASINS; SIMULATED DISCHARGES; SOURCES OF UNCERTAINTY; SPATIAL DIFFERENCES; TIME SLICE; UNCERTAINTY ESTIMATES; VALIDATION DATA; VARIABLE INFILTRATION CAPACITIES; WIND SPEED;

BAYESIAN NETWORKS; CLIMATE CHANGE; COMPUTER SIMULATION; MATHEMATICAL MODELS; PROBABILITY DISTRIBUTIONS; RUNOFF; WATER SUPPLY; WATERSHEDS;

UNCERTAINTY ANALYSIS;

BAYESIAN ANALYSIS; CALIBRATION; CLIMATE CHANGE; CLIMATE EFFECT; ERROR CORRECTION; HYDROLOGICAL CHANGE; HYDROLOGICAL MODELING; HYDROLOGICAL REGIME; HYDROLOGICAL RESPONSE; MARKOV CHAIN; MONTE CARLO ANALYSIS; PARAMETERIZATION; PRECIPITATION INTENSITY; RIVER BASIN; RIVER DISCHARGE; RUNOFF; SPATIAL VARIATION; UNCERTAINTY ANALYSIS;

INDIA;

EID: 84865714687     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2011WR011123     Document Type: Article

References (64)

1. Abdulla, F., and D. Lettenmaier (1997), Development of regional parameter estimation equations for a macroscale hydrologic model, J. Hydrol., 197, 230-257, doi:10.1016/S0022-1694(96)03262-3.
2. Arnell, N. W. (1999), A simple water balance model for the simulation of streamflow over a large geographic domain, J. Hydrol., 217, 314-335, doi:10.1016/S0022-1694(99)00023-2.
3. Arora, V. K. (2001), Streamflow simulations for continental-scale river basins in a global atmospheric general circulation model, Adv. Water Resour., 24, 775-791, doi:10.1016/S0309-1708(00)00078-6.
4. Atkinson, S. E., R. A. Woods, and M. Sivapalan (2002), Climate and landscape controls on water balance model complexity over changing timescales, Water Resour. Res., 38(12), 1314, doi:10.1029/2002WR001487.
5. Bates, B. C., and E. P. Campbell (2001), A Markov chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall-runoff modeling, Water Resour. Res., 37, 937-947, doi:10.1029/2000WR900363.
6. Barnett, T. P. et al. (2008), Human-Induced Changes in the Hydrology of the Western United States, Science, 319(5866), 1080-1083. doi:10.1126/ science.1152538.
7. Beven, K. J., and A. M. Binley (1992), The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Processes, 6, 279-298, doi:10.1002/hyp.3360060305.
8. Beven, K. J., J. Freer, B. Hankin, and K. Schulz (2000), The use of generalized likelihood measures for uncertainty estimation in high order models of environmental systems, in Nonlinear and Nonstationary Signal Processing, edited by W. J. Fitzgerald et al., pp. 115-151, Cambridge Univ. Press, New York.
9. Bras, R. A. (1990), Hydrology, An Introduction to Hydrologic Science, Addison Wesley, Reading, North Carolina.
10. Chib, S., and E. Greenberg (1995), Understanding the Metropolis-Hastings algorithm, Am. Stat., 49, 327-335.
11. Dadhwal, V. K., S. P. Aggarwal, and N. Mishra (2010), Hydrological simulation of Mahanadi river basin and impact of land use / land cover change on surface runoff using a macro scale hydrological model, in ISPRS TC VII Symposium-100 Years ISPRS, Vienna, Austria, July 5-7, 2010, vol. 38, Part 7B, edited by W. Wagner and B. Székely, pp. 165-170
12. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (IAPRS), Vienna, Austria.
13. Demaria, E. M., B. Nijssen, and T. Wagener (2007), Monte Carlo sensitivity analysis of land surface parameters using the Variable Infiltration Capacity model, J. Geophys. Res., 112, D11113, doi:10.1029/2006JD007534.
14. Dunn, S. M. (1998), Large scale modeling using small scale processes, in Hydrology in a Changing Environment, vol. 1, edited by H. Wheater and C. Kirby, pp. 11-20, Wiley, Chichester, UK.
15. Engeland, K., and L. Gottschalk (2002), Bayesian estimation of parameters in a regional hydrological model, Hydrol. Earth Syst. Sci., 6(5), 883-898, doi:10.5194/hess-6-883-2002.
16. Engeland, K., C.-Y. Xu, and L. Gottschalk (2005), Assessing uncertainties in a conceptual water balance model using Bayesian methodology, Hydrol. Sci. J., 50(1), 45-63, doi:10.1623/hysj.50.1.45.56334.
17. Franks, S., K. J. Beven, P. F. Quinn, and I. Wright (1997), On the sensitivity of soil-vegetation-atmosphere transfer (SVAT) schemes: Equifinality and the problem of robust calibration, Agric. For. Meteorol., 86, 63-75, doi:10.1016/S0168-1923(96)02421-5.
18. Gallagher, M., and J. Doherty (2007), Parameter estimation and uncertainty analysis for a watershed model, Environ. Model. Softw., 22(7), 1000-1020, doi:10.1016/j.envsoft.2006.06.007.
19. Gan, T. Y., and G. F. Biftu (1996), Automatic calibration of conceptual rainfallrainoff models: Optimization algorithms, catchment conditions, and model structure, Water Resour. Res., 32(12), 3513-3524, doi:10.1029/95WR02195.
20. Gelman, A., and D. B. Rubin (1992), Inference from iterative simulation using multiple sequences, Statistical Science, 7, 457-511.
21. Geweke, J. (1992), Evaluating the accuracy of sampling-based approaches to calculating posterior moments, in Bayesian Statistics 4, edited by J. M. Bernardo et al., pp. 163-193, Clarendon Press, Oxford, UK.
22. Gupta, H. V., S. Sorooshian, and P. O. Yapo (1998), Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res., 34, 751-763, doi:10.1029/ 97WR03495.
23. Hansen, M. C., R. S. DeFries, J. R. G. Townshend, and R. Sohlberg (2000), Global land cover classification at 1 km spatial resolution using a classification tree approach, Int. J. Remote Sens., 21, 1331-1364, doi:10.1080/ 014311600210209.
24. Hastings, W. K. (1970), Monte Carlo sampling methods using Markov chains and their applications, Biometrika, 57, 97-109.
25. Hersbach, H. (2000), Decomposition of the continuous ranked probability score for ensemble prediction systems, Weather Forecast., 15(5), 559-570, doi:10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2.
26. Hidalgo, H. G. et al. (2009), Detection and Attribution of Streamflow Timing Changes to Climate Change in the Western United States, J. Clim., 22, 3838-3855, doi:10.1175/2009JCLI2470.
27. Huang, M., and X. Liang (2006), On the assessment of the impact of reducing parameters and identification or parameter uncertainties for a hydrologic model with applications to ungaged basins, J. Hydrol., 320, 37-61, doi:10.1016/j.jhydrol.2005.07.010.
28. Jin, X., C.-Y. Xu, Q. Zhang, and V. P. Singh (2010), Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model, J. Hydrol., 383(3-4), 147-155, doi:10.1016/ j.jhydrol.2009.12.028.
29. Jiang, T., Y. D. Chen, C.-Y. Xu, X. Chen, X. Chen, and V. P. Singh (2007), Comparison of hydrological impacts of climate change simulated by six hydrological models in the Dongjiang Basin, South China, J. Hydrol., 336, 316-333.
30. Kalnay, E., et al. (1996), The NCEP/NCAR 40-year reanalysis project, Bull. Am. Meteorol. Soc., 77, 437-471, doi:10.1175/1520-0477(1996)077<0437: TNYRP>2.0.CO;2.
31. Kavetski, D., G. Kuczera, and S. W. Franks (2006), Bayesian analysis of input uncertainty in hydrological modeling: 2. Application, Water Resour. Res., 42, W03408, doi:10.1029/2005WR004376.
32. Kimball, J. S., S. W. Running, and R. Nemani (1997), An improved method for estimating surface humidity from daily minimum temperature, Agric. For. Meteorol., 85, 87-98, doi:10.1016/S0168-1923(96)02366-0.
33. Krause, P., D. P. Boyle, and F. Bäse (2005), Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89-97, doi:10.5194/adgeo-5-89-2005.
34. Kuczera, G. (1983), Improved parameter inference in catchment models. 1. Evaluating parameter uncertainty, Water Resour. Res. 19(5), 1151-1172.
35. Kuczera, G., and E. Parent (1998), Monte Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm, J. Hydrol., 211, 69-85, doi:10.1016/S0022-1694(98)00198-X.
36. Leung, L. R., A. F. Hamlet, D. P. Lettenmaier, and A. Kumar (1999), Simulations of the ENSO hydroclimate signals in the Pacific Northwest Columbia River basin, Bull. Am. Meteorol. Soc., 80, 2313-2329, doi:10.1175/1520- 0477(1999)080<2313:SOTEHS>2.0.CO;2.
37. Liang, X., and J. Guo (2003), Intercomparison of land-surface parameterization schemes: Sensitivity of surface energy and water fluxes to model parameters, J. Hydrol., 279, 182-209, doi:10.1016/S0022-1694(03)00168-9.
38. Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges (1994), A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res., 99, 14,415-14,428, doi:10.1029/94JD00483.
39. Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges (1996), Onedimensional statistical dynamic representation of subgrid spatial variability of precipitation in the two-layer variable infiltration capacity model, J. Geophys. Res., 101, 21,403-21,422, doi:10.1029/96JD01448.
40. Lohmann, D., R. Nolte-Holube, and E. Raschke (1996), A large-scale horizontal routing model to be coupled to land surface parameterization schemes, Tellus, 48A, 708-721.
41. Lohmann, D., E. Raschke, B. Nijssen, and D. P. Lettenmaier (1998), Regional scale hydrology: I. Formulation of the VIC-2L model coupled to a routing model, Hydrol. Sci. J., 43(1), 131-141.
42. Mantovan, P., and E. Todini (2006), Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology, J. Hydrol., 330, 368-381, doi:10.1016/j.jhydrol.2006.04.046.
43. Maurer, E. P., A. W. Wood, J. D. Adam, and D. P. Lettenmaier (2002), A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States, J. Clim., 15, 3237-3251, doi:10.1175/1520- 0442(2002)015<3237:ALTHBD>2.0.CO;2.
44. Metropolis, N., A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller (1953), Equations of state calculations by fast computing machines, J. Chem. Phys., 21, 1087-1091.
45. Nijssen, B., D. P. Lettenmaier, X. Liang, S. W. Wetzel, and E. F. Wood (1997), Streamflow simulation for continental-scale river basins, Water Resour. Res., 33, 711-724, doi:10.1029/96WR03517.
46. Nijssen, B., G. M. O'Donnell, D. P. Lettenmaier, D. Lohmann, and E. F. Wood (2001a), Predicting the discharge of global rivers, J. Clim., 14, 3307-3323, doi:10.1175/1520-0442(2001)014<3307:PTDOGR>2.0.CO;2.
47. Nijssen, B., R. Schnur, and D. P. Lettenmaier (2001b), Global restrospective estimation of soil moisture using the VIC land surface model, 1980-93, J. Clim., 14, 1790-1808, doi:10.1175/1520-0442(2001)014< 1790:GREOSM>2.0.CO;2.
48. Raje, D., and P. P. Mujumdar (2009), A conditional random field based downscaling method for assessment of climate change impact on multisite daily precipitation in the Mahanadi basin, Water Resour. Res., 45, W10404, doi:10.1029/2008WR007487.
49. Raje, D., and P. P. Mujumdar (2010), Constraining uncertainty in regional hydrologic impacts of climate change: Nonstationarity in downscaling, Water Resour. Res., 46, W07543, doi:10.1029/2009WR008425.
50. Refsgaard, J. C., and B. Storm (1996), Construction calibration and validation of hydrological models, in Distributed Hydrological Modelling, Water Science and Technology Library, vol. 22, edited by M. B. Abbott and J. C. Refsgaard, pp. 41-54, Kluwer Acad., Dordrecht, Netherlands,doi:10.1007/978-94- 009- 0257-2-3.
51. Sorooshian, S., and V. K. Gupta (1983), Automatic calibration of conceptual rainfall-runoff models: The question of parameter observability and uniqueness, Water Resour. Res., 19(1), 260-268, doi:10.1029/WR019i001p00260.
52. Stedinger, J. R., R. M. Vogel, S. U. Lee, and R. Batchelder (2008), Appraisal of the generalized likelihood uncertainty estimation (GLUE) method, Water Resour. Res., 44, W00B06, doi:10.1029/2008WR006822.
53. Thornton, P. E., and S. W. Running (1999), An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation, Agric. For. Meteorol., 93, 211-228, doi:10.1016/S0168-1923(98)00126-9.
54. Tierney, L. (1994), Markov chains for exploring posterior distributions, Ann. Stat., 22, 1701-1762.
55. Toth, Z., O. Talagrand, G. Candille, and Y. Zhu (2003), Probability and ensemble forecasts, in Forecast Verification: A Practitioners' Guide in Atmospheric Science, edited by I. T. Jolliffe and D. B. Stephenson, pp. 37-164, John Wiley and Sons, Chichester, UK.
56. Vörösmarty, C. J., B. Moore, A. L. Grace, M. P. Gildea, J. M. Melillo, B. J. Peterson, E. B. Rastetter, and P. A. Steudler (1989), Continental scale models of water balance and fluvial transport: An application to South America, Global Biogeochem. Cycles, 3, 241-265, doi:10.1029/GB003i003p00241.
57. Widén-Nilsson, E., S. Halldin, and C.-Y. Xu (2007), Global water-balance modelling with WASMOD-M: Parameter estimation and regionalisation, J. Hydrol., 340(1-2), 105-118, doi:10.1016/j.jhydrol.2007.04.002.
58. Wood, E. F., D. P. Lettenmaier, and V. G. Zartarian (1992), A land-surface hydrology parameterization with subgrid variability for General Circulation Models, J. Geophys. Res., 97(D3), 2717-2728, doi:10.1029/91JD01786.
59. Wood, E. F., D. P. Lettenmaier, X. Liang, B. Nijssen, and S. W. Wetzel (1997), Hydrological modeling of continental-scale basins, Annu. Rev. Earth Planet. Sci., 25, 279-300, doi:10.1146/annurev.earth.25.1.279.
60. Wood, A. W., E. P. Maurer, A. Kumar, and D. P. Lettenmaier (2002), Long-range experimental hydrologic forecasting for the eastern United States, J. Geophys. Res., 107(D20), 4429, doi:10.1029/2001JD000659.
61. Wood, A. W., L. R. Leung, V. Sridhar, and D. P. Lettenmaier (2004), Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs, Clim. Change, 62, 189-216, doi:10.1023/B:CLIM.0000013685. 99609.9e.
62. Wooldridge, S. A., J. D. Kalma, and J. P. Walker (2003), Importance of soil moisture measurements for inferring parameters in hydrological models of low-yielding ephemeral catchments, Environmental Modelling andSoftware, 18, 35-48.
63. Xu, C.-Y. (1999), Climate change and hydrologic models: A review of existing gaps and recent research developments, Water Resour. Manage., 13, 369-382, doi:10.1023/A:1008190900459.
64. Yatagai, A., O. Arakawa, K. Kamiguchi, H. Kawamoto, M. I. Nodzu, and A. Hamada (2009), A 44-year daily gridded precipitation dataset for Asia based on a dense network of rain gauges, Sci. Online Lett. Atmos., 5, 137-140, doi:10.2151/sola.2009-035.