Multi-variable sensitivity and identifiability analysis for a complex environmental model in view of integrated water quantity and water quality modeling.

Environmental models are often over-parameterized. A sensitivity analysis can identify influential model parameters for, e.g. the parameter estimation process, model development, research prioritization and so on. This paper presents the results of an extensive study of the Latin-Hypercube-One-factor-At-a-Time (LH-OAT) procedure applied to the Soil and Water Assessment Tool (SWAT). The LH-OAT is a sensitivity analysis method that can be categorized as a screening method. The results of the sensitivity analyses for all output variables indicate that the SWAT model of the river Kleine Nete is mainly sensitive to flow related parameters. Rarely, water quality parameters get a high priority ranking. It is observed that the number of intervals used for the Latin-Hypercube sampling should be sufficiently high to achieve converged parameter rankings. Additionally, it is noted that the LH-OAT method can enhance the understanding of the model, e.g. on the use of water quality input data.

[1]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[2]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[3]  Fred Worrall,et al.  Sensitivity analysis and identification of the best evapotranspiration and runoff options for hydrological modelling in SWAT-2000 , 2007 .

[4]  M. J. Hall,et al.  Rainfall-Runoff Modelling , 2004 .

[5]  Gilbert T. Bernhardt,et al.  A comprehensive surface-groundwater flow model , 1993 .

[6]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice , 2002 .

[7]  S. Grunwald,et al.  A global sensitivity analysis tool for the parameters of multivariable catchment models , 2006 .

[8]  Indrajeet Chaubey,et al.  Sensitivity and identifiability of stream flow generation parameters of the SWAT model , 2010 .

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

[10]  K. Beven.  Rainfall-Runoff Modelling: The Primer , 2012 .

[11]  A. van Griensven,et al.  Autocalibration in hydrologic modeling: Using SWAT2005 in small-scale watersheds , 2008, Environ. Model. Softw..

[12]  R. Srinivasan,et al.  A global sensitivity analysis tool for the parameters of multi-variable catchment models , 2006 .

[13]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[14]  K. Loague.  Rainfall-Runoff Modelling , 2010 .

[15]  K.,et al.  Nonlinear sensitivity analysis of multiparameter model systems , 1977 .

[16]  Jeffrey G. Arnold,et al.  The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions , 2007 .

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

[18]  Max D. Morris,et al.  Factorial sampling plans for preliminary computational experiments , 1991 .

[19]  G. Hornberger,et al.  Approach to the preliminary analysis of environmental systems , 1981 .

[20]  Peter C. Young,et al.  The data-based mechanistic approach to the modelling, forecasting and control of environmental systems , 2006, Annu. Rev. Control..

[21]  M. B. Beck,et al.  On the problem of model validation for predictive exposure assessments , 1997 .

[22]  Andrea Saltelli,et al.  An effective screening design for sensitivity analysis of large models , 2007, Environ. Model. Softw..