Sensitivity Analysis for an Infiltration-Runoff Model with Parameter Uncertainty

Abstract

Evaluation of the uncertainty effect of input parameters on model outputs is presented. HYDROL-INF, an infiltration-runoff model for layered soils, is used for simulating infiltration and surface runoff. The predictive uncertainty related to the modeling is evaluated. Specifically, a three-step procedure is implemented for sensitivity analysis of the model. The first step involves application of the local sensitivity analysis to gain a qualitative ranking of the whole set of input parameters for different model outputs with a relatively low computational cost. In the second step, the first-order second moment (FOSM) method is used to obtain the most sensitive parameters to the output from the parameters identified by the local sensitivity analysis. Third, the robust and computationally efficient Fourier amplitude sensitivity test (FAST) is conducted to overcome the nonlinearity problem for estimating the uncertainty of the model. Furthermore, the proposed methodology is applied to a three-layer soil system with varying permeability under unsteady rainfall. The soil system consists of loamy sand, sandy loam, and clay loam. The model outputs considered herein include cumulative infiltration and runoff. Dissimilar lists of crucial parameters are identified by using the FOSM and FAST methods. It is found that the combination of different soil layers and rainfall conditions have significant influences on the variance contribution of individual parameters to the total variance of the model.