## 5 Conclusions  
LandscapeDNDC with the two different tree growth modules, PnET and PSIM, was evaluated for application in an oak forest in southern England by modifying a limited set of species parameters that define tree physiology. The PnET module simulated annual GPP, TER and NEP rates averaged over 1999-2007 to within 8.5%, 6.7% and 14.1%, respectively, of values derived from EC measurements, which is within the estimated uncertainty of the EC method of 10-15% suggested by [PERSON] et al. (2018). The PSIM module, which included an understorey component, simulated these mean annual data to within < 1%. For both modules, the measured inter-annual variability of GPP, TER and NEP was not well simulated, and comparison with measurements indicated negative modelling efficiency (ME) values. However, when monthly CO\({}_{2}\) fluxes were compared, the model efficiency for PnET and PSIM improved to positive values. These annual and monthly comparisons suggest that while the main processes in the forest CO\({}_{2}\) balance are included, neither module has appropriate sensitivity to key drivers to respond to interannual variations. Annual soil CO\({}_{2}\) fluxes were consistently underestimated by PnET by 32% compared with average soil chamber measurements, but were overestimated by 26% by PSIM. Measured soil N\({}_{2}\)O fluxes exhibited considerable inter-annual variation of 15.4-156 mg N m\({}^{-2}\) year\({}^{-1}\), which was not reproduced by PnET or PSIM, but in both cases simulated annual totals were of the same order of magnitude as measurements (PnET: 38.9-55.2 mg N m\({}^{-2}\) year\({}^{-1}\) and PSIM: 36.9-60.3 mg N m\({}^{-2}\) year\({}^{-1}\)). Comparison between monthly simulated and measured N\({}_{2}\)O soil flux data showed poor agreement for both modules. For soil CO\({}_{2}\) fluxes, PSIM produced better results when compared with measurements from 2008-2012. For soil N\({}_{2}\)O fluxes, LandscapeDNDC was most sensitive to initialisation values of bulk density, which also affects soil organic carbon content and field capacity. Of the climate variables, decreasing the temperature by 10% had a greater effect on N\({}_{2}\)O emissions than increasing temperature or changing precipitation by 10%. The PnET parameter to which simulated soil N\({}_{2}\)O and CO\({}_{2}\) fluxes were most sensitive, increasing over the duration of the simulation, was GDFDGLEND, which defines the cumulative temperature required to reach maximum leaf area. There was no single PSIM parameter to which simulated fluxes were particularly sensitive, but inclusion of the understorey contributed 14-22% to simulated flux outputs of GPP, TER, soil CO\({}_{2}\) and N\({}_{2}\)O. The simulation of forest thinning showed that PSIM produced a better match to measured GPP and NEP data than PnET, although the latter simulated a more realistic response for the TER rate through increased soil respiration. Both modules reproduced monthly variations in TER well for two separate years following thinning (PnET ME = 0.46 and 0.18; PSIM ME = 0.45 and 0.60), but neither module reproduced the details of the measured time course. The simulated increases in N\({}_{2}\)O gas emissions could not be assessed accurately but agreed broadly with observations made at the site by [PERSON] and [PERSON] (2010). This study shows that the LandscapeDNDC model can simulate monthly and averaged annual ecosystem CO\({}_{2}\) fluxes measured at the Straits Inclosure well when key parameters have been optimised for local conditions. The PSIM module performed better than PnET and its ability to simulate the significant understorey component present at the site may have contributed to the improvement, although there is insufficient information to provide robust understorey parameter values or to evaluate the understorey simulation. While monthly soil CO\({}_{2}\) fluxes have been shown to be well simulated by PSIM, particularly compared to 2008-2012 measurements, N\({}_{2}\)O fluxes and variability were not.