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

0
- By: ,

Courtesy of IWA Publishing

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.

Keywords: environmental modeling, integrated modeling, Latin-Hypercube–One-factor-At-a-Time, model parameters, sensitivity analysis, SWAT

Customer comments

No comments were found for Multi-variable sensitivity and identifiability analysis for a complex environmental model in view of integrated water quantity and water quality modeling. Be the first to comment!