# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 3 Results and Discussion
### Exploration of Soil Moisture and Soil Water Potential: Spatiotemporal Trends
Figure 12: Interpolated surfaces (Kriging method) showing the average seasonal variation in volumetric soil moisture (SM) and soil water potential (WP), based on data retrieved from 2010 to 2016. (**a**) winter average (December–February): (**a1**) SM at 10 cm; (**a2**) SM at 30 cm; (**a3**) WP at 30 cm; (**b**) spring average (March–May): (**b1**) SM at 10 cm; (**b2**) SM at 30 cm; (**b3**) WP at 30 cm; (**c**) summer average (June–August): (**c1**) SM at 10 cm; (**c2**) SM at 30 cm; (**c3**) WP at 30 cm; (**d**) fall average (September–November): (**d1**) SM at 10 cm; (**d2**) SM at 30 cm; (**d3**) WP at 30 cm. SM is expressed in m\({}^{3}\cdot\)m\({}^{-3}\). WP is expressed in kPa. The elevation contours are expressed in m. The dots represent the nodes from which the data was retrieved. Another noticeable fact is that the average variation in SM is higher at 10 cm than at 30 cm, suggesting that the longer travel time to deeper soil reduces the spatial variability of SM. SM is lower during the summer than in the other seasons, which is consistent with the recorded daily rainfall data at the Pittsburgh Airport Meteorological Station [91] for the 2010-2016 period, where there is a dry period between the end of the spring and summer months (i.e., May-September). Finally, based on the SM surfaces from summer and fall (Figure 12c1,c2,d1,d2), there seems to exist a water pathway (darker color in the surface) from the highest elevation to the lower part of the area, located at the right side of the SM surface. This pathway might be explained by the natural surface and subsurface water movement towards the creek located to the south of the study area which, in turn, drains into the pond (immediately downstream of the region with higher SM). Topography showed stronger influence on SM during the winter. Regarding WP, the interpolated surfaces show a variable behavior from one season to another, but WP is mostly higher in the regions with higher elevations, even though there are some lower regions with higher WP. Figure 13 illustrates the improvement achieved by the network expansion. In highly complex and heterogeneous environments, the amount and quality of data is proportional to the amount of extractable knowledge [92]. SM and WP interpolated surfaces were created for the average fall conditions in 2010 and 2016. Additional surfaces were generated for the average of 11 August 2016, which is the day with the highest recorded alive nodes (102 nodes, including the relays). In general, these surfaces show similar patterns for each corresponding variable (i.e., SM at 10 cm and 30 cm and WP at 30 cm). However, the 2016 network size, with less scattering in the node locations, provides better estimations than the 2010 network size. It is observed that the patterns for fall 2016 and 11 August 2016 are more similar to each other than the patterns for fall 2010 (see Figure 13). This indicates that the higher node density in 2016 provides more detailed insights of the temporal and spatial variability of SM and WP. Overall, the analysis has shown the applicability of WSNs for short and long term hydrological patterns characterization at the catchment scale, for steep-forested environments. In addition, Table 4 shows the RMSE obtained from the OK interpolation for the scenarios presented in Figures 12 and 13. The RMSE has the same units as the analyzed variable (i.e., SM or WP). The number of nodes from which the data was extracted, along with the maximum and minimum SM and WP values are also included. In terms of the SM, it is observed that RMSE is lower at 30 cm than at 10 cm, which is consistent with what has been shown before (i.e., that the SM is much more variable in the near-surface soil than in the deeper soil). In the case of the different average seasonal conditions, even with a different density of nodes within the site, the RMSE did not experience a significant change, thus showing the robustness of the OK interpolation method.