A Comprehensive Sensitivity Analysis Framework for Model Evaluation and Improvement Using a Case Study of the Rangeland Hydrology and Erosion Model

Abstract

The complexity of numerical models and the large numbers of input factors result in complex interdependencies of sensitivities to input parameter values, and high risk of having problematic or nonsensical model responses in localized regions of the input parameter space. Sensitivity analysis (SA) is a useful tool for ascertaining model responses to input variables. One popular method is local SA, which calculates the localized model response of output to an input parameter. This article describes a comprehensive SA method to explore the parameter behavior globally by calculating localized sensitivity indices over the entire parameter space. This article further describes how to use this framework to identify model deficiencies and improve model function. The method was applied to the Rangeland Hydrology and Erosion Model (RHEM) using soil erosion response as a case study. The results quantified the localized sensitivity, which varied and was interdependently related to the input parameter values. This article also shows that the localized sensitivity indices, combined with techniques such as correlation analysis and scatter plots, can be used effectively to compare the sensitivity of different inputs, locate sensitive regions in the parameter space, decompose the dependency of the model response on the input parameters, and identify nonlinear and incorrect relationships in the model. The method can be used as an element of the iterative modeling process whereby the model response can be surveyed and problems identified and corrected in order to construct a robust model.