## 4 Results
### 2D ER Models
The 2D ER models identify well (i) the vertical distribution of Holocene and Pleistocene formations deduced in Figure 5 and its horizontal interruptions mostly due to faults; and (ii) the general groundwater types, both low-salinity Pleistocene and moderate salinity Holocene in sites 1 and 2 and saltwater Pleistocene and freshwater Holocene in site 3 as conceptualized in Figure 2. In site 3, sequential time-lapse ERT3 enabled to (i) infer geology due to interruption in VS acquisition at distances 130-220 m in MASW3, and (ii) disambiguate aquifer H from Pleistocene sand dunes having similar VS \(<\) 200 m s\({}^{-1}\) (Figure 5b). In detail, most of ER values in aquifer H are 20-40 \(\Omega\) m in ERT1 (inland site) and in ERT2, and 40-60 \(\Omega\) m in ERT3 (coastal site). This ER distribution apparently contradicts the expected decreasing inland gradient of GEC inferred by the decreasing inland gradient of atmospheric bulk deposition EC reported in the southwest coast of Portugal [39]. Thus, another process such as low-salinity (high-resistivity) groundwater transference from the hydraulically connected underlying Pleistocene formation through faults is proposed to explain this ER pattern, as illustrated in ERT2 (Figure 6b). Most of ER values in Pleistocene formations are 50-70 \(\Omega\) m in ERT1 (inland site) and in ERT2, and 1-20 \(\Omega\) m in ERT3 (coastal site) (Figure 6). In site 3, the lowest ER values in Pleistocene formation are attributed to the saltwater lens conceptualized in Figure 2, whereas ER values in the 20-40 \(\Omega\) m range are attributed to a thin freshwater-saltwater interface (Figure 6c). As above described, ER changes inside steady ER shapes are associated to transient GER changes due to water transferences and fluxes varying over time, both including natural processes such as stream flow recharge, preferential flow through faults, lateral recharge, and freshwater discharge over the saltwater lens, as well as human-induced actions such as irrigation return and organic matter-rich leaching (Figure 6). In order to quantify how and where natural processes and human-induced actions modify ER over time, in each site, nodal ER data from March, June, and December lapses (\(z\)) were compared to nodal ER data from the September lapse (\(z\)*). The relative difference is:  
\[RD=(z-z^{\text{*}})/z^{\text{*}}. \tag{3}\]  
RD is mapped in Figure 6. The rationale for reference lapse choosing was that geological formations must have the lowest GEC mass flow and storage in order to minimize the influence of groundwater transference among them. September 2014 was the selected reference lapse for comparisons (Figure 4c). RD values enable to show both natural processes and human-induced actions modifying ER. Statistics of time-lapse ER models and RD values are in Table 6. Average ER follows the same evolution in sites 1 and 2, with higher values in March and June and lower in December regarding those observed in September. In site 3, lower ER values in March are attributed to the delayed influence of brackish groundwater discharge stored after the SAL opened, whereas in December 2014 (this site cannot be surveyed), the same evolution to sites 1 and 2 is presumed. It is important to note that ER models (Table 6) are delayed regarding atmospheric bulk deposition EC values (Figure 4b). This fact corroborates that average groundwater turnover time is higher than three months, long after the dry phase (December 2014), because the ER evidences of current aquifer recharge and groundwater transference from underlying geological formations have not yet arrived to aquifer H. The coefficient of variation of ER evidences this temporal predictability.