Automatic Calibration and Predictive Uncertainty Analysis of a Semidistributed Watershed Model

Semidistributed models are commonly calibrated manually, but software for automatic calibration is now available. We present a two-stage routine for automatic calibration of the semidistributed watershed model Soil and Water Assessment Tool (SWAT) that finds the best values for the model parameters, preserves spatial variability in essential parameters, and leads to a measure of the model prediction uncertainty. In the first stage, a modified global Shuffled Complex Evolution (SCE-UA) method was employed to find the “best” values for the lumped model parameters. In the second stage, the spatial variability of the original model parameters was restored and a local search method (a variant of Levenberg–Marquart method) was used to find a more distributed set of parameters using the results of the previous stage as starting values. A method called “regularization” was adopted to prevent the parameters from taking extreme values. In addition, we applied a nonlinear calibration-constrained method to develop confidence intervals for annual and 7-d average flow predictions. We calibrated stream flow in the Etowah River measured at Canton, GA (a watershed area of 1580 km 2 ) for the years 1983 to 1992 and used the years 1993 to 2001 for validation. The Parameter Estimator (PEST) software was used to conduct the two-stage automatic calibration and prediction uncertainty analysis. Calibration for daily and monthly flow produced a very good fit to the measured data. Nash-Sutcliffe coefficients for daily and monthly flow over the calibration period were 0.60 and 0.86, respectively. They were 0.61 and 0.87, respectively, for the validation period. The nonlinear prediction uncertainty analysis worked well for long-term (annual) flow in that our prediction confidence intervals included or were very near to the observed flow for most years. It did not work well for short-term (7-d average) flows in that the prediction confidence intervals did not include the observed flow, especially for low and high flow conditions.

[1]  John Doherty,et al.  Ground Water Model Calibration Using Pilot Points and Regularization , 2003, Ground water.

[2]  S. Sorooshian,et al.  Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data , 1996 .

[3]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information , 1998 .

[4]  W. J. Shuttleworth,et al.  Parameter estimation of a land surface scheme using multicriteria methods , 1999 .

[5]  Soroosh Sorooshian,et al.  Evaluation and Transferability of the Noah Land Surface Model in Semiarid Environments , 2005 .

[6]  Richard L. Cooley,et al.  Simultaneous confidence and prediction intervals for nonlinear regression models with application to a groundwater flow model , 1987 .

[7]  Mark E. Borsuk,et al.  On Monte Carlo methods for Bayesian inference , 2003 .

[8]  Jeffrey G. Arnold,et al.  Automatic calibration of a distributed catchment model , 2001 .

[9]  S. Sorooshian,et al.  Effective and efficient algorithm for multiobjective optimization of hydrologic models , 2003 .

[10]  Soroosh Sorooshian,et al.  Optimal use of the SCE-UA global optimization method for calibrating watershed models , 1994 .

[11]  M. B. Beck,et al.  Water quality modeling: A review of the analysis of uncertainty , 1987 .

[12]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods , 2000 .

[13]  P. Reichert,et al.  A comparison of techniques for the estimation of model prediction uncertainty , 1999 .

[14]  Keith Beven,et al.  Changing ideas in hydrology — The case of physically-based models , 1989 .

[15]  A. Jakeman,et al.  How much complexity is warranted in a rainfall‐runoff model? , 1993 .

[16]  Willy Bauwens,et al.  Multiobjective autocalibration for semidistributed water quality models , 2003 .

[17]  Soroosh Sorooshian,et al.  Multi-objective global optimization for hydrologic models , 1998 .

[18]  Per Christian Hansen,et al.  REGULARIZATION TOOLS: A Matlab package for analysis and solution of discrete ill-posed problems , 1994, Numerical Algorithms.

[19]  Henrik Madsen,et al.  Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. , 2000 .

[20]  S. Sorooshian,et al.  Shuffled complex evolution approach for effective and efficient global minimization , 1993 .

[21]  S. Sorooshian,et al.  A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters , 2002 .

[22]  J. Doherty,et al.  METHODOLOGIES FOR CALIBRATION AND PREDICTIVE ANALYSIS OF A WATERSHED MODEL 1 , 2003 .

[23]  D. Legates,et al.  Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .

[24]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[25]  S. Sorooshian,et al.  Evaluation of Maximum Likelihood Parameter estimation techniques for conceptual rainfall‐runoff models: Influence of calibration data variability and length on model credibility , 1983 .

[26]  Keith Beven,et al.  Prophecy, reality and uncertainty in distributed hydrological modelling , 1993 .

[27]  Willem Bouten,et al.  Toward Improved Identifiability of Soil Hydraulic Parameters: On the Selection of a Suitable Parametric Model , 2003 .

[28]  Wesley W. Wallender,et al.  Inverse modeling of large-scale spatially distributed vadose zone properties using global optimization / W06503, doi:10.1029/2003WR002706 , 2004 .

[29]  John R. Williams,et al.  LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT 1 , 1998 .

[30]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[31]  George H. Hargreaves,et al.  Agricultural Benefits for Senegal River Basin , 1985 .

[32]  S. Walsh,et al.  Status and restoration of the Etowah River, an imperiled Southern Appalachian Ecosystem , 1997 .

[33]  S. Sorooshian,et al.  A multistep automatic calibration scheme for river forecasting models , 2000 .