Quasi-Monte Carlo (QMC) points are a substitute for plain Monte (MC) that greatly improve integration accuracy under mild assumptions on the problem. Because QMC can give errors o(1/n) as $$n\rightarrow \infty $$ , and randomized versions attain root mean squared o(1/n), changing even one point change estimate by an amount much larger than error would have been worsen convergence rate. As resul...

Implementation of sensitivity analysis (SA) procedures is helpful in calibration of models and also for their transposition to different watersheds. The reported studies on SA of Soil and Water Assessment Tool (SWAT) model were mostly focused on identifying parameters for pruning or modifying during the calibration process. This paper presents a sensitivity and identifiability analysis of model...

Sparse grid interpolation is widely used to provide good approximations to smooth functions in high dimensions based on relatively few function evaluations. By using an efficient conversion from the interpolating polynomial provided by evaluations on a sparse grid to a representation in terms of orthogonal polynomials (gPC representation), we show how to use these relatively few function evalua...

The global sensitivity analysis of a complex numerical model often calls for the estimation of variance-based importance measures, named Sobol’ indices. Metamodelbased techniques have been developed in order to replace the cpu time-expensive computer code with an inexpensive mathematical function, which predicts the computer code output. The common metamodel-based sensitivity analysis methods a...

The variance-based Sobol' approach is one of the few global sensitivity analysis methods that is suitable for complex models with spatially distributed inputs. Yet it needs a large number of model runs to compute sensitivity indices: in the case of models where some inputs are 2D Gaussian random fields, it is of great importance to generate a relatively small set of map realizations capturing m...

Proper specification of model parameters is critical to the performance of land surface models (LSMs). Due to high dimensionality and parameter interaction, estimating parameters of an LSM is a challenging task. Sensitivity analysis (SA) is a tool that can screen out the most influential parameters on model outputs. In this study, we conducted parameter screening for six output fluxes for the C...

Three sampling methods are compared for efficiency on a number of test problems of various complexity for which analytic quadratures are available. The methods compared are Monte Carlo with pseudo-random numbers, Latin Hypercube Sampling, and Quasi Monte Carlo with sampling based on Sobol’ sequences. Generally results show superior performance of the Quasi Monte Carlo approach based on Sobol’ s...

Global sensitivity analysis is used to quantify the influence of input variables on a numerical model output. Sobol’ indices are now classical sensitivity measures. However their estimation requires a large number of model evaluations, especially when interaction effects are of interest. Derivative-based global sensitivity measures (DGSM) have recently shown their efficiency for the identificat...

We prove in a constructive way that multivariate integration in appropriate weighted Sobolev classes is strongly tractable and the e-exponent of strong tractability is 1 (which is the best-possible value) under a stronger assumption than Sloan and Wo! zniakowski’s assumption. We show that quasi-Monte Carlo algorithms based on the Sobol sequence and Halton sequence achieve the convergence order ...