Global sensitivity study of non-reactive and sorptive solute dispersivity in multiscale heterogeneous sediments

Abstract

Lagrangian-based transport models provide effective ways of understanding mass transport processes within aquifer systems. The models provide a direct relationship between sparse data on sedimentary architecture (e.g., facies proportions and mean lengths) and physical and geochemical sediment properties (e.g., hydraulic conductivity (K) and sorption distribution coefficient (Kd)) to transport observables such as dispersion. Data sparsity leads to parameter uncertainty, which in turn makes model prediction uncertain. This study identifies the key uncertain inputs for both non-reactive and sorptive solute dispersivity through a global sensitivity analysis. Estimates of the individual and correlation contributions of input parameters to model output are provided. Data from two sites with different scales of heterogeneities are used to evaluate the sensitive parameters of non-reactive dispersivity at different scales. The results show that sorptive solute dispersivity is most sensitive to in-facies mean Kd, followed by Kd variance, while non-reactive plume dispersivity is most sensitive to in-facies mean K, followed by the volume proportions and mean lengths of facies types. When the heterogeneity integral scale increases to 102-103m, hydraulic gradient becomes a non-negligible factor controlling the non-reactive solutes transport. The convergence of the sensitivity indices and the effect of different sampling methods on the results are also evaluated in this study. The results show that the number of input parameters and the complexity of the model determine the sampling size to achieve the ranking convergence of the sensitive indices. Sobol sequence sampling scheme outperforms in terms of convergence rate and accuracy over the Monte Carlo and Latin Hypercube sampling schemes. The results of this study will improve our understanding of the complex model system, and also provide guidance for further field investigation and data collection.