## 4 Discussion  
### Simulation of Environmental Conditions  
As soil temperature and soil moisture are major controls on GHG fluxes from soils, accurate simulation of these values is important, especially in the uppermost soil layers (0-30 cm) where most biological activity takes place [55; 56]. Simulated soil temperature, using both PnET and PSIM, had a lower annual amplitude than measured data for most years. The only year studied when simulated temperature amplitude exceeded measured amplitude, 2013, had an anomalously dry summer (68 mm, compared with average summer rainfall of 167 mm) and a cold Jan-Mar (average temperature of 3.3 \({}^{\circ}\)C, compared with the equivalent for 1995-2013 of 5.8 \({}^{\circ}\)C), which suggests a link with climatic conditions. DNDC calculates soil temperature from soil properties including thermal conductivity derived from a combination of solid and water phases, depending on moisture content [34]. It seems that when the moisture content is high, the simulation of soil temperature is less accurate. The small difference of \(<\)1 \({}^{\circ}\)C in extreme values of PSIM and PnET soil temperatures is probably generated in the ECM air chemistry module due to differences in canopy structure, which causes shading, and evapotranspiration, before soil surface temperatures are calculated in the DNDC soil microclimate module. Soil moisture measurements at the Straits Inclosure Tower Site have been unreliable at times, especially following dry conditions in the clay-rich soils when cracks reduce the probe accuracy, and spatial variation from proximity to vegetation is expected (relative standard error was 1-6%). As with soil temperature, simulated soil moisture generally showed a reduced seasonal range, with the exception of peaks (often one day long) following heavy rainfall events. The original aim of DNDC was to predict seasonal or annual N\({}_{2}\)O emissions [16] and therefore the timing of simulated rain events was not important: they start at midnight and continue at the same pre-defined intensity until daily rainfall has finished [37]. The vegetation module takes account of canopy interception and evapotranspiration, and then rainwater saturates the soil, layer by layer, at a rate determined by each layer's soil hydraulic conductivity (K). Altering K at model setup changes soil moisture during \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**PnET**} & \multicolumn{2}{c}{**PSIM**} \\
**Level of Thinning:** & **0\%** & **30\%** & **0\%** & **30\%** \\ \hline
**2009** & & & & \\ RMSE (\%) & 28.8 \({}^{*}\) & 39.2 \({}^{*}\) & 36.5 \({}^{*}\) & 39.3 \({}^{*}\) \\ Average total error & 31.7 & 57.9 & 40.1 & 58.2 \\ Modelling efficiency & 0.73 & 0.46 & 0.56 & 0.45 \\ Coefficient of determination & 0.61 & 0.83 & 0.69 & 1.16 \\ Correlation coefficient, r & 0.92 & 0.85 & 0.88 & 0.80 \\ No. of values & 12 & 12 & 12 & 12 \\ \hline
**2012** & & & & \\ RMSE (\%) & 39.4 \({}^{*}\) & 48.1 \({}^{*}\) & 26.5 \({}^{*}\) & 33.5 \({}^{*}\) \\ Average total error & 54.9 & 68.8 & 37.1 & 47.8 \\ Modelling efficiency & 0.51 & 0.18 & 0.78 & 0.60 \\ Coefficient of determination & 0.91 & 0.78 & 1.31 & 1.19 \\ Correlation coefficient, r & 0.91 & 0.90 & 0.89 & 0.92 \\ No. of values & 12 & 12 & 12 & 12 \\ \hline \hline \end{tabular} \({}^{*}\) No significant total error.  
\end{table} 
Table 7: Results from ModEval statistical analysis [46] of monthly TER data for 2009 and 2012 simulated with PnET and PSIM and compared with eddy covariance measurements at the Straits Inclosure. Results from modelling with 0% thin are compared with measurements from the western, unthinned sector and those from modelling with 30% thin are compared with the eastern, thinned sector (EC data from [45]). and after rainfall events, but a 100-fold change in K (from 0.0006 to 0.06 mm min\({}^{-1}\)) only changed simulated soil moisture by 1-2% on most days. Results with the higher input K were still 2-3% different from the measured values and did not affect the simulated soil temperature.