An alternative way to compute Fourier amplitude sensitivity test (FAST)
Computational Statistics & Data Analysis
Statistical methods for sensitivity and performance analysis in computer experiments
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
An effective screening design for sensitivity analysis of large models
Environmental Modelling & Software
Environmental Modelling & Software
Sensitivity analysis of spatial models
International Journal of Geographical Information Science
Computational Statistics & Data Analysis
How to avoid a perfunctory sensitivity analysis
Environmental Modelling & Software
Sensitivity analysis for complex ecological models - A new approach
Environmental Modelling & Software
Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis
Environmental Modelling & Software
Environmental Modelling & Software
Moment-independent regional sensitivity analysis: Application to an environmental model
Environmental Modelling & Software
The spatial framework for weight sensitivity analysis in AHP-based multi-criteria decision making
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Hi-index | 0.00 |
Efficient sensitivity analysis, particularly for the global sensitivity analysis (GSA) to identify the most important or sensitive parameters, is crucial for understanding complex hydrological models, e.g., distributed hydrological models. In this paper, we propose an efficient integrated approach that integrates a qualitative screening method (the Morris method) with a quantitative analysis method based on the statistical emulator (variance-based method with the response surface method, named the RSMSobol' method) to reduce the computational burden of GSA for time-consuming models. Using the Huaihe River Basin of China as a case study, the proposed approach is used to analyze the parameter sensitivity of distributed time-variant gain model (DTVGM). First, the Morris screening method is used to qualitatively identify the parameter sensitivity. Subsequently, the statistical emulator using the multivariate adaptive regression spline (MARS) method is chosen as an appropriate surrogate model to quantify the sensitivity indices of the DTVGM. The results reveal that the soil moisture parameter WM is the most sensitive of all the responses of interest. The parameters Kaw and g"1 are relatively important for the water balance coefficient (WB) and Nash-Sutcliffe coefficient (NS), while the routing parameter RoughRss is very sensitive for the Nash-Sutcliffe coefficient (NS) and correlation coefficient (RC) response of interest. The results also demonstrate that the proposed approach is much faster than the brute-force approach and is an effective and efficient method due to its low CPU cost and adequate degree of accuracy.