Peer Review10.5194/hess-2023-183-ac1
Reply on RC1
Meirav Cohen
- 05 Oct 2023
TL;DR: GSA is a powerful tool for analyzing transport models and identifying the most influential parameters for groundwater contamination from fuel derivatives. The study emphasizes the importance of degradation rate and clay layers in predicting aquifer contamination.
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Abstract: <strong class="journal-contentHeaderColor">Abstract.</strong> One of the greatest threats to groundwater is contamination from fuel derivatives. Benzene, a highly mobile and toxic fuel derivative, can easily reach groundwater from fuel sources and lead to extensive groundwater contamination and drinking water disqualification. Modelling benzene transport in the unsaturated zone can provide quantification of the risk for groundwater contamination and needed remediation. Yet, characterization of the problem is often complicated, due to typical soil heterogeneity and numerous unknown site and solute parameters, as well as the difficulty in distinguishing important from non-important parameters, resulting in high uncertainty. Thus, the application of sensitivity analysis (SA) methods, such as global SA (GSA), is required to reduce uncertainty and detect important parameters for groundwater contamination, mitigation, and remediation. Nevertheless, studies devoted to identification of the driving parameters for fuel derivatives transport in the unsaturated zone are scarce. Here, we performed GSA on a problem of benzene transport in the unsaturated zone. First, a simple GSA ‘Morris’ screening method was used for a homogenous sandy vadose zone. Then, a more computationally-demanding ‘Sobol’ variance-based GSA was run on the most influential parameters. Finally, the ‘Morris’ method was tested for a heterogeneous medium containing clay layers. To overcome the problem of model crashes during GSA, several methods were tested for imputation of missing data. The GSA results found benzene degradation rate (<em>λ<sub>k</sub></em>) to be the utmost influential parameter controlling benzene mobility. The depth of aquifer followed in importance in the homogenous media, while in the heterogeneous media parameters related to the clay layers, such as clay adsorption coefficient and the number of clay layers, followed in importance. The study emphasizes the significance of <em>λ<sub>k</sub></em> and the presence of clay layers in predicting aquifer contamination. The study also strengthen the importance of heterogenous media representation in the GSA, since different parameters control the transport in different soil layers. Overall, GSA is demonstrated here as an important tool for the analysis of transport models. The results also show that in higher dimensionality models, the radial basis function (RBF) is an efficient surrogate model for missing data imputation.
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Figures
Table 2 Input parameters and their range used for GSA: 178 
Figure 7 – Morris analysis results for heterogeneous media for: a. Cumulative benzene flux to the aquifer and b. Final benzene concentration in the aquifer. Following these influential parameters, the clay layers’ dispersivity , and of the sand layers, are less influential but still somewhat close to the middle of the graph. The least influential parameters are mostly soil properties of the clay layers, such 530 as the clay hydraulic conductivity , and parameters, and the dispersivity of the sand layers . Interestingly, Gribb et al. (2002), who conducted LSA for a risk assessment model of benzene and naphtelene transport to groundwater through sand, loam, and clay soils, also reported high model sensitivity to and for all soils. In their case, the model was also less sensitive to , except for pure loam and clay soils. For other parameters that were not tested here (porosity, bulk density, residual water content and initial concentration), the model was only sensitive in the case of pure clayey soil. Yet, 535 their study assumed homogenous media of each soil type. Here, most clay parameters are less influential, probably due to the smaller fraction of clay, as compared with the sand layers, which comprise most of the profile. 
Figure 3 - Morris analysis results for homogenous sandy soil with RBF imputation for: A. Cumulative benzene flux to the aquifer 385 and B. Final benzene concentration in the aquifer. 
Table 3 – Summary of clay layers distribution 
Table 1 Constant input parameters for the model: 
Figure 6 – Morris analysis results for heterogeneous media, µ* values for: a. cumulative benzene flux to the aquifer; b. final benzene concentration in the aquifer. In Figures 7a and 7b, the effect of the different parameters on the outputs, in terms of versus , when using the RBF method, is shown. Like the GSA results of the homogenous media, it is evident that the degradation coefficients of both soils and the van Genuchten parameter of the sand layers are the most dominant factors controlling benzene transport to the 500 aquifer. The degradation coefficient of the sand layers was found to be the most dominant parameter, considering both the cumulative flux and the concentration at the end of the simulation. Next, the degradation coefficient of the clay layers and share the second and third places of influence. While was found to be more influential for the flux, had a stronger effect on the concentration. This makes sense since primarily affects water flow, whereas affects benzene directly. In the Morris homogenous analysis also affected the flux more than the concentration. Therefore, although the 505 two parameters are substantial, we see that the relative effect also depends on the output tested. We note that was also third/fourth in significance for homogenous soil analysis; thus, it is a very significant parameter for modeling the transport of
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