Technical Papers
Oct 4, 2013

Second-Order Autoregressive Model-Based Likelihood Function for Calibration and Uncertainty Analysis of SWAT Model

Publication: Journal of Hydrologic Engineering
Volume 20, Issue 2

Abstract

Second order autoregressive [AR(2)] model has been adopted in the likelihood function to calibrate the soil and water assessment tool (SWAT) model for the Canard River watershed, southwestern Ontario, Canada. The Bayesian approach is used for uncertainty analysis of SWAT modeling. The performance of AR(2) model for uncertainty estimation is evaluated by the index called Percentage of observations bracketed by the unit confidence interval (PUCI) for 95% confidence limits. The results are compared with the simple least square (SLS) method of calibration. In the SLS method, the modeling errors are assumed to be uncorrelated. The study reveals that the model parameter uncertainty is high and there exists local optimum values in the parameter space. The reliability of streamflow simulation uncertainty due to parameter uncertainty is increased when AR(2) model is implemented in the calibration process. The comparison of PUCI values between AR(2) method and SLS method shows that the estimation of streamflow simulation uncertainty is more reliable in AR(2) model-based calibration method. But the lower values of PUCI indicate very high uncertainty in 95% confidence limits estimation. The residuals are observed to have nonnormal distribution with nonconstant variance. Therefore, appropriate transformation of data might improve the uncertainty estimation. The model structural uncertainty is high for simulating streamflow in the study area during low-flow and high-flow periods. Therefore, the study suggests applying separate statistical error models in the likelihood function for representing the modeling errors in low-flow and high-flow periods.

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Acknowledgments

Authors would like to express special thanks to Dr. Jasper A. Vrugt for the source codes of SCEM-UA algorithm. The authors thankfully acknowledge the financial support extended by Natural Sciences and Engineering Research Council of Canada (NSERC) through Discovery Grant to the senior author.

References

Abbaspour, K. C. (2008). SWAT-CUP2: SWAT calibration and uncertainty programs—A user manual, Eawag, Duebendorf, Switzerland.
ArcGIS version 9.2 [Computer software]. Esri, Redlands, CA.
Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R. (1998). “Large area hydrologic modeling and assessment. Part I: Model development.” J. Am. Water Resour. Assoc., 34(1), 73–89.
Baker, R. A., and Culver, T. B. (2010). “Locating nested monitoring wells to reduce model uncertainty for management of a multilayer coastal aquifer.” J. Hydrol. Eng., 15(10), 763–771.
Bates, B. C., and Campbell, E. P. (2001). “A Markov chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall-runoff modeling.” Water Resour. Res., 37(4), 937–947.
Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (2008). Time series analysis: Forecasting and control, Wiley, New Jersey.
Delleur, J. W., Tao, P. C., and Kavvas, M. L. (1976). “An evaluation of the practicality and complexity of some rainfall and runoff time series models.” Water Resour. Res., 12(5), 953–970.
Duan, Q., Sorooshian, S., and Ibbitt, R. (1988). “A maximum likelihood criterion for use with data collected at unequal time intervals.” Water Resour. Res., 24(7), 1163–1173.
Engeland, K., Renard, B., Steinsland, I., and Kolberg, S. (2010). “Evaluation of statistical models for forecast errors from the HBV model.” J. Hydrol., 384(1–2), 142–155.
Feyen, L., Vrugt, J. A., Nualláin, B. Ó., van der Knijff, J., and De Roo, A. (2007). “Parameter optimisation and uncertainty assessment for large-scale streamflow simulation with the LISFLOOD model.” J. Hydrol., 332(3–4), 276–289.
Freni, G., Mannina, G., and Viviani, G. (2011). “The role of modeling uncertainty in the estimation of climate and socio-economic impact on river water quality.” J. Water Resour. Plann. Manage., 138(5), 479–490.
Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2004). Bayesian data analysis, 2nd Ed., Chapman & Hall/CRC.
Gelman, A., and Rubin, D. B. (1992). “Inference from iterative simulation using multiple sequences.” Stat. Sci., 7(4), 457–472.
GNU Octave version 3.2.3 [Computer software]. 〈http://www.gnu.org〉.
Hantush, M. M., and Kalin, L. (2008). “Stochastic residual-error analysis for estimating hydrologic model predictive uncertainty.” J. Hydrol. Eng., 13(7), 585–596.
Jin, X., Xu, C., Zhang, Q., and Singh, V. P. (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.
Kavetski, D., Kuczera, G., and Franks, S. W. (2006). “Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory.” Water Resour. Res., 42(3), W03407.
Kuczera, G. (1983). “Improved parameter inference in catchment models. 1. Evaluating parameter uncertainty.” Water Resour. Res., 19(5), 1151–1162.
Laloy, E., Fasbender, D., and Bielders, C. L. (2010). “Parameter optimization and uncertainty analysis for plot-scale continuous modeling of runoff using a formal Bayesian approach.” J. Hydrol., 380(1–2), 82–93.
Li, L., Xu, C., Xia, J., Engeland, K., and Reggiani, P. (2011). “ Uncertainty estimates by Bayesian method with likelihood of AR (1) plus normal model and AR (1) plus multi-normal model in different time-scales hydrological models.” J. Hydrol., 406(1–2), 54–65.
Li, Z., Xu, Z., Shao, Q., and Yang, J. (2009). “Parameter estimation and uncertainty analysis of SWAT model in upper reaches of the Heihe river basin.” Hydrol. Process., 23(19), 2744–2753.
McLeod, A. I., Hipel, K. W., and Lennox, W. C. (1977). “Advances in Box-Jenkins modeling 2. Applications.” Water Resour. Res., 13(3), 577–586.
Muleta, M. K. (2011). “ Model performance sensitivity to objective function during automated calibrations.” J. Hydrol. Eng., 17(6), 756–767.
Nash, J. E., and Sutcliffe, J. V. (1970). “River flow forecasting through conceptual models. Part I—A discussion of principles.” J. Hydrol., 10(3), 282–290.
Park, D., Loftis, J. C., and Roesner, L. A. (2011). “Performance modeling of storm water best management practices with uncertainty analysis.” J. Hydrol. Eng., 332–344.
Reichert, P., and Mieleitner, J. (2009). “Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters.” Water Resour. Res., 45(10), W10402.
Schaefli, B., Talamba, D. B., and Musy, A. (2007). “Quantifying hydrological modeling errors through a mixture of normal distributions.” J. Hydrol., 332(3–4), 303–315.
Shrestha, R. R., and Franz Nestmann, F. (2009). “Physically based and data-driven models and propagation of input uncertainties in river flood prediction.” J. Hydrol. Eng., 1309–1319.
Siddiqui, M. M. (1958). “On the inversion of the sample covariance matrix in a stationary autoregressive process.” Ann. Math. Stat., 29(2), 585–588.
Smith, T., Sharma, A., Marshall, L., Mehrotra, R., and Sisson, S. (2010). “Development of a formal likelihood function for improved Bayesian inference of ephemeral catchments.” Water Resour. Res., 46, W12551.
Vrugt, J. A. (2004). “Towards improved treatment of parameter uncertainty in hydrologic modeling.” Ph.D. thesis, Universiteit van Amsterdam, Amsterdam, Netherlands.
Vrugt, J. A., Gupta, H. V., Bouten, W., and Sorooshian, S. (2003). “A shuffled complex evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters.” Water Resour. Res., 39(8), 1201.
Vrugt, J. A., ter Braak, C. J. F., Gupta, H. V., and Robinson, B. A. (2009). “Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?” Stochastic Environ. Res. Risk Assess., 23(7), 1011–1026.
Yang, J., Abbaspour, K. C., and Reichert, P. (2005). “Interfacing SWAT with systems analysis tools: A generic platform.” 3rd Int. SWAT Conf. Proc., R. Srinivasan, J. Jacobs, D. Day, and K. Abbaspour, eds., Zurich, Switzerland, 169–178.
Yang, J., Reichert, P., Abbaspour, K. C., and Yang, H. (2007). “Hydrological modelling of the Chaohe basin in China: Statistical model formulation and Bayesian inference.” J. Hydrol., 340(3–4), 167–182.

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Go to Journal of Hydrologic Engineering
Journal of Hydrologic Engineering
Volume 20Issue 2February 2015

History

Received: Nov 4, 2011
Accepted: Oct 2, 2013
Published online: Oct 4, 2013
Discussion open until: Dec 31, 2014
Published in print: Feb 1, 2015

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Authors

Affiliations

Arpana Rani Datta [email protected]
Post-Doctoral Fellow, Dept. of Civil and Environmental Engineering, Univ. of Windsor, Windsor, ON, Canada N9B 3P4. E-mail: [email protected]
Tirupati Bolisetti [email protected]
Associate Professor, Dept. of Civil and Environmental Engineering, Univ. of Windsor, Windsor, ON, Canada N9B 3P4 (corresponding author). E-mail: [email protected]

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