# 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
The lowest RMSE in SM, for both depths, obtained for 11 August 2016, suggests that a shorter period of time and a higher density of nodes reduce the uncertainty of the SM estimation. The estimated RMSE in the WP surfaces showed more variability than in the case of SM, mostly due to larger differences in the WP ranges for the analyzed conditions. However, if considering the error as a percentage of the range, the 11 August 2016 and fall 2016 average scenarios have the lowest percentages, 0.061% and 0.098%, respectively. In summary, geostatistical tools such as the OK interpolation constitute an important complement to WSNs for environmental monitoring purposes, especially when it is intent to estimate the spatiotemporal behavior of hydrological parameters. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**SM (m\({}^{3}\)-m\({}^{-3}\)) at 10 cm**} & \multicolumn{4}{c}{**SM (m\({}^{3}\)-m\({}^{-3}\)) at 30 cm**} & \multicolumn{4}{c}{**WP (kPa) at 30 cm**} \\ \cline{2-13}
**Interpolated Surface** & \begin{tabular}{c} **\#** \\ **Nodes** \\ \end{tabular} & \begin{tabular}{c} **Max** \\ **SM** \\ \end{tabular} & \begin{tabular}{c} **Min** \\ **SM** \\ \end{tabular} & \begin{tabular}{c} **RMSE** \\ **SMD** \\ \end{tabular} & \begin{tabular}{c} **\#** \\ **SM** \\ \end{tabular} & \begin{tabular}{c} **Max** \\ **SMD** \\ \end{tabular} & \begin{tabular}{c} **Min** \\ **Nodes** \\ \end{tabular} & \begin{tabular}{c} **\#** \\ **WP** \\ \end{tabular} & \
\begin{tabular}{c} **Max** \\ **WP** \\ \end{tabular} &
\begin{tabular}{c} **Min** \\ **WP** \\ \end{tabular} \\ \hline Winter 2010-2016 & 38 & 0.570 & 0.180 & 0.061 & 38 & 0.320 & 0.200 & 0.030 & 37 & \(-10\) & \(-17\) & \(2.12\) \\ Spring 2010-2016 & 55 & 0.410 & 0.120 & 0.062 & 57 & 0.300 & 0.130 & 0.048 & 51 & \(-8\) & \(-31\) & \(4.94\) \\ Summer 2010-2016 & 71 & 0.330 & 0.070 & 0.061 & 72 & 0.280 & 0.050 & 0.052 & 59 & \(-22\) & \(-99\) & \(17.46\) \\ Fall 2010-2016 & 72 & 0.530 & 0.040 & 0.063 & 72 & 0.290 & 0.060 & 0.051 & 57 & \(-13\) & \(-59\) & \(6.01\) \\ Fall 2010 & 24 & 0.190 & 0.110 & 0.059 & 23 & 0.190 & 0.030 & 0.058 & 24 & \(-19\) & \(-418\) & \(41.91\) \\ Fall 2016 & 72 & 0.500 & 0.050 & 0.026 & 72 & 0.390 & 0.040 & 0.057 & 69 & \(-25\) & \(-207\) & \(17.88\) \\ \(8/11/2016\) & 74 & 0.290 & 0.006 & 0.031 & 74 & 0.140 & 0.030 & 0.024 & 74 & \(-38\) & \(-472\) & \(26.53\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMSE of the interpolated surfaces. Figure 13: Interpolated surfaces (Kriging method) showing a comparison between the average fall (September–November) SM and WP of 2010 and 2016, and average SM and WP on 11 August 2016 (08/11/2016). (**a**) SM at 10 cm (fall 2010); (**b**) SM at 30 cm (fall 2010); (**c**) WP at 30 cm (fall 2010); (**d**) SM at 10 cm (fall 2016); (**e**) SM at 30 cm (fall 2016); (**f**) WP at 30 cm (fall 2016); (**g**) SM at 10 cm (11 August 2016); (**h**) SM at 30 cm (11 August 2016); (**i**) WP at 30 cm (11 August 2016). SM is expressed in m\({}^{3}\).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