Simulating Long-Term Development of Greenhouse Gas Emissions, Plant Biomass, and Soil Moisture of a Temperate Grassland Ecosystem under Elevated Atmospheric CO2

The rising atmospheric CO2 concentrations have effects on the worldwide ecosystems such as an increase in biomass production as well as changing soil processes and conditions. Since this affects the ecosystem’s net balance of greenhouse gas emissions, reliable projections about the CO2 impact are required. Deterministic models can capture the interrelated biological, hydrological, and biogeochemical processes under changing CO2 concentrations if long-term observations for model testing are provided. We used 13 years of data on above-ground biomass production, soil moisture, and emissions of CO2 and N2O from the Free Air Carbon dioxide Enrichment (FACE) grassland experiment in Giessen, Germany. Then, the LandscapeDNDC ecosystem model was calibrated with data measured under current CO2 concentrations and validated under elevated CO2. Depending on the hydrological conditions, different CO2 effects were observed and captured well for all ecosystem variables but N2O emissions. Confidence intervals of ensemble simulations covered up to 96% of measured biomass and CO2 emission values, while soil water content was well simulated in terms of annual cycle and location-specific CO2 effects. N2O emissions under elevated CO2 could not be reproduced, presumably due to a rarely considered mineralization process of organic nitrogen, which is not yet included in LandscapeDNDC.


Introduction
To date, biogeochemical cycles all over the world are undergoing fundamental adjustments as a response to rising atmospheric greenhouse gas (GHG) concentrations and related climatic and plant physiological consequences [1]. Especially carbon dioxide (CO 2 ) has received widespread attention due to its role in the global radiation budget, making its increasing concentrations the main driver for

Study Site
The investigated temperate, permanent grassland is part of the "Environmental Monitoring and Climate Impact Research Station Linden" near Giessen, Germany (50 • 32 N, 8 • 41.3 E, 172 m a.s.l.). Local climatic conditions are characterized by annual precipitation of 563 mm and an average air temperature of 9.5 • C in the period from April 1995 to December 2011. The grassland was cultivated while the soil remained undisturbed for over 100 years and was not irrigated during the investigated period. The vegetation has been described as an Arrhenatheretum elatioris Br.-Bl. Filipendula ulmaria sub-community with up to 35 different plant species dominated by tall oat grass, yellow oat grass, meadow soft grass, meadow geranium, and white beadstraw on a stagnofluvic gleysol on loamy-sandy sediments over clay [28]. The top soil layers (A-horizon 0-12 cm) show a partially low bulk density (0.63-1.01 g cm −3 ), which, however, increases rapidly with soil depth (up to 1.52 g cm −3 at 15 cm, Agronomy 2020, 10, 50 3 of 17 see [29]). The grassland has been managed extensively with mineral fertilizer application (ammonium nitrate, NH 4 NO 3 ) of 40 kg ha −1 yr −1 in mid-April and two cuts per year.
The grassland research area was established in 1993/94, and in May 1998, the Giessen Free Air Carbon dioxide Enrichment (GiFACE) experiment started to investigate the effects of rising atmospheric CO 2 concentrations [29]. We used data collected from a total of six plots, each surrounded by a fumigation ring. The three plots E1, E2, and E3 were fumigated with additional CO 2 (=elevated CO 2 , eCO 2 ), while the remaining three plots A1, A2, and A3 were not fumigated for control purposes (=ambient CO 2 , aCO 2 ). The experiment is designed in such a way that the fumigation is dynamically adjusted to about 20% above the ambient concentration. In reality, the average CO 2 concentrations were 22.3% higher for E1-E3 (annual means: 458 ppm in 1999 up to 484 ppm in 2011) compared to A1-A3 (386 ppm/401 ppm) in the evaluation period. The evaluation period started in August 1998 with the onset of CO 2 emission measurements and ended in December 2011. In addition, data about weather conditions (global radiation, air temperature, relative humidity, precipitation), N deposition, groundwater level and nitrate (NO 3 ) concentrations, vertically resolved soil properties (texture, bulk density, pH, organic C and N content), soil water content (SWC, measured at soil depth 0-15 cm with TDR (time domain reflectometry) probes [30]), plant harvest (aboveground biomass, C/N ratio, cutting schedule), and CO 2 and nitrous oxide (N 2 O) emissions (both measured at dusk) are used for this study. Emissions were measured with opaque static chambers placed on permanently installed frames (10 cm deep) and analyzed on a gas chromatograph (for details, see [31]). Since the opacity impedes photosynthesis related carbon fluxes during emission measurements, we considered the measured CO 2 emissions to be total ecosystem respiration minus growth respiration.
The plots were paired in three blocks A1/E1, A2/E2, and A3/E3 with different hydrological characteristics. We described the plots according to their respective average groundwater levels (agl) as driest (A1/E1, agl = −1.02 m), medium (A3/E3, agl = −0.76 m), and wettest (A2/E2, agl = −0.66 m) pairs. Groundwater is generally shallow and highly variable in this area, rising close to the surface in most winter periods. A slight north facing slope (ca. 2%) indicates potential lateral inflow of groundwater from the upslope croplands. Regular measurement of groundwater NO 3 − concentrations was started in 2016 with several measurements per month at seven different points on the test site, with the exception of January. No such data are available for the observation period of this study. Hence, the annual cycle from 2016 was used, interpolated and averaged over all seven measuring points of the test site as a proxy. More about relevant measurement data can be found at http://www.face2face.center (last access on 11 November 2019) and Table A1.

Data Implementation
All measured data used for model simulations (see Table A1) are classified according to four different categories: model initialization, model forcing data, model calibration data, and model parameters. Initialization uses data to set up initial values of organic C and N content of the soil at the beginning of the simulation. Forcing data determine the boundary conditions of the simulation. They include management events (cutting, fertilizer application), weather data, N deposition, atmospheric CO 2 concentrations, groundwater NO 3 − concentrations, and groundwater levels (interpolated to daily time resolution). Calibration data are measured data belonging to five target variables that are used for sensitivity analysis, calibration, and/or validation. They include harvested plant biomass, plant C-N ratio (only sensitivity analysis and calibration), SWC, and emissions of CO 2 and N 2 O. Parameters are ecosystem properties whose values are either fixed or calibrated within predefined ranges. Fixed parameters were derived from literature (soil texture, pH, and bulk density). Calibrated parameters include only soil hydraulic properties (field capacity, wilting point, hydraulic conductivity, vanGenuchten α and n) whose ranges were derived from literature (field capacity, wilting point), expert knowledge (α, n), and ad hoc infiltration experiments (sks). It should be noted that the behavioral simulations were performed for both aCO 2 and eCO 2 with a steeper gradient in saturated hydraulic conductivity sks in the uppermost soil layers. The gradient Agronomy 2020, 10, 50 4 of 17 corresponds to sks values obtained during recent ad hoc infiltration measurements, which were ignored only during the sensitivity analysis to prevent unintended false positive selection of sks as a sensitive parameter. Ranges to calibrate field capacity and wilting point for each of the three blocks A1/E1, A2/E2, and A3/E3 were derived from water retention curves (see [32]).

Model Setup
LandscapeDNDC [27,33] is an ecosystem model framework that provides an exchangeable pool of submodels for the description of various compartments. We selected canopyECM [34], PlaMo x , MeTr x , and EcHy according to their suitability with regard to the CO 2 effect. PlaMo x [35] is based on the Farquhar model [36] and is used for plant physiology and photosynthesis, which allows the simulation of the CO 2 effect on biomass production. MeTr x [37] was selected to model the biogeochemistry, making it possible to simulate the (indirect) effect of eCO 2 on soil microbiology and associated greenhouse gas emissions. EcHy ( [38], under review) was implemented to simulate both hydrology and its relationship to plant development, making ecohydrological effects of eCO 2 on soil water content visible. MeTr x and EcHy were supplemented by additional algorithms that simulate the influence of groundwater, including NO 3 − dissolved in groundwater, on soil hydrology and chemistry [26].
The runtime of the simulations began in April 1995, whereby the three years before the start of the evaluation period in August 1998 were used as spin-up. All submodels ran consecutively with a two-hourly resolution. Since we considered a grassland ecosystem on a plot scale, we used a laterally homogeneous LandscapeDNDC setup. Vertical resolution was set to 50 mm for the upper soil layers (0-20 cm), increasing to 150-200 mm for the lower layers (50-100 cm). While the layers were different in respect of their respective soil properties, each layer was divided into several sublayers sharing the same characteristics.

Sensitivity Analysis and Calibration
The LandscapeDNDC setup we used contains a subset of more than 100 indefinite parameters, i.e., parameters whose values are uncertain within limits chosen by expert knowledge and literature. For these parameters, a set of values had to be estimated to give the best possible agreement between simulation and measurement data under aCO 2 . In this context, aCO 2 measurement data were used exclusively for calibration, while validation was based only on eCO 2 data. The agreement was quantified by using Root Mean Squared Error (RMSE) as a target function.
As the number of necessary model runs grows exponentially with the number of parameters, a sensitivity analysis was performed at the beginning to reduce the number of parameters to the most sensitive. We used the Fourier Amplitude Sensitivity Test (FAST, see [39,40]) to determine the most sensitive parameters under aCO 2 conditions on the basis of 250,000 simulations each (for details, see Figure A1 and Table A2).
For the remaining sensitive parameters (n = 16), 250,000 parameter sets for A1, A2, and A3 were calculated and simulated again using Latin Hypercube Sampling (LHS, see [41]), while all other parameters were left at their initial values. From the simulations generated by LHS, a selection of the "best" (which we call behavioral from here on) simulations based on General Likelihood Uncertainty Estimation (GLUE, see [42]) was made. GLUE allows the calibration of LandscapeDNDC to an ensemble of suitable (that is, behavioral) simulations that meet previously defined threshold objective function criteria (see Houska et al. [24]). These thresholds were chosen as upper limits for the RMSE of the behavioral simulations, meaning that each behavioral simulation must have an RMSE for each target variable less than or equal to the set value. Starting with the values by Houska et al., a further lowering was carried out, during which the subsequent threshold values (Table 1) were determined by simultaneous manual adjustment at all target variables and aCO 2 plots.  [43]) was used for all sensitivity analyses and calibration runs.

Evaluation
For the assessment of the CO 2 effect in the ensemble simulations, it had to be determined to what extent such an effect could also be proven in observation data. Statistical tests were performed to determine whether significant differences between aCO 2 and eCO 2 were present in the respective measurement data. Since the measurements were not normally distributed, we chose the nonparametric Mann-Whitney U-test to assess the probability of whether the data from aCO 2 and eCO 2 plots could belong to the same distribution.
The model ensembles of the behavioral simulations both for aCO 2 and eCO 2 were summarized as cumulative diagrams (except SWC) and compared with the measured data. The ensemble simulations were aggregated into confidence intervals with a confidence coefficient of 95%. For a better quantitative evaluation, the RMSE values of the behavioral (non-cumulative) simulations for aCO 2 and eCO 2 (see Table A3) were averaged and compared. Non-cumulative measured data were used for statistical significance tests (see Table A4) and assessed on a significance level of α = 0.05.

Results
We evaluated the target variables of plant biomass, SWC, CO 2 , and N 2 O emissions. The plant C/N ratio was only used for calibration and is therefore not evaluated in detail here. Average values of the simulated C/N ratios were between 24.7 and 27.6, which is about the same range as the average measured values (25.0-28.3).

Cumulative Plant Biomass
Limits of the confidence intervals increased from aCO 2 to eCO 2 for all blocks, including almost all (96%) cumulative measured biomass values for A2, E2, A3, and E3 ( Figure 1). However, significantly increased biomass due to eCO 2 was only measured for the driest block (A1/E1, see Tables A4 and A5) but not for the wetter blocks (A2/E2 and A3/E3). For block A1/E1, percentage of cumulative measured values within the confidence intervals remained stable (37%/41%), even though average RMSE increased moderately (+18% , Table A3) under eCO 2 . Since all measured values of A1 and E1 were close to the upper edge of their respective confidence intervals, a slight underestimation by the simulations remained at this block both for aCO 2 and eCO 2 .

Soil Water Content
The simulations generally followed the dynamics of the observations, though the confidence intervals did not include the majority of the measured SWC values (see Figure 2). Short-term fluctuations of the SWC as well as drying phases were well captured by the simulations. Longer periods in which the soil was saturated were primarily seen in the winter months. This occurred in the simulations as well as in the measurements but at a higher level for the latter, which is why peak values of the SWC in these periods were often underestimated. This applied to both aCO2 and eCO2, even though the measurement curves diverged strongly in some cases, with SWC being up to twice as high under eCO2 as under aCO2.

Soil Water Content
The simulations generally followed the dynamics of the observations, though the confidence intervals did not include the majority of the measured SWC values (see Figure 2). Short-term fluctuations of the SWC as well as drying phases were well captured by the simulations. Longer periods in which the soil was saturated were primarily seen in the winter months. This occurred in the simulations as well as in the measurements but at a higher level for the latter, which is why peak values of the SWC in these periods were often underestimated. This applied to both aCO 2 and eCO 2 , even though the measurement curves diverged strongly in some cases, with SWC being up to twice as high under eCO 2 as under aCO 2 .

Cumulative CO2 Emissions
As can be seen in Figure 3, measured and simulated CO2 emissions were higher in all blocks under eCO2 than under aCO2. The increase was significant for A1/E1 and A3/E3 but not for A2/E2 (Table A4 and Table A5). Confidence intervals included fewer cumulative measured values under eCO2 than under aCO2 treatment. The proportions ranged from 94% to 44% at block A1/E1, from 79% to 65% at A2/E2, and from 70% to 69% at A3/E3. Outliers almost exclusively occurred before 2004, with the exception of E1, where the confidence interval was below the measurements until 2007. For both aCO2 and eCO2, all outliers were close to the upper edge of the confidence range, indicating an underestimation of LandscapeDNDC during the first half of the simulation period. No deviations occurred after 2007. Average RMSE values increased under eCO2, with a moderate increase from A1 to E1 (about 20%) and a slight increase from A3 to E3 (about 9%). However, RMSE (Table A3) at E2 remained virtually unchanged compared to A2 (increase of about 1%).

Cumulative CO 2 Emissions
As can be seen in Figure 3, measured and simulated CO 2 emissions were higher in all blocks under eCO 2 than under aCO 2 . The increase was significant for A1/E1 and A3/E3 but not for A2/E2 (Tables A4  and A5). Confidence intervals included fewer cumulative measured values under eCO 2 than under aCO 2 treatment. The proportions ranged from 94% to 44% at block A1/E1, from 79% to 65% at A2/E2, and from 70% to 69% at A3/E3. Outliers almost exclusively occurred before 2004, with the exception of E1, where the confidence interval was below the measurements until 2007. For both aCO 2 and eCO 2 , all outliers were close to the upper edge of the confidence range, indicating an underestimation of LandscapeDNDC during the first half of the simulation period. No deviations occurred after 2007. Average RMSE values increased under eCO 2 , with a moderate increase from A1 to E1 (about 20%) and a slight increase from A3 to E3 (about 9%). However, RMSE (Table A3) at E2 remained virtually unchanged compared to A2 (increase of about 1%).

Cumulative CO2 Emissions
As can be seen in Figure 3, measured and simulated CO2 emissions were higher in all blocks under eCO2 than under aCO2. The increase was significant for A1/E1 and A3/E3 but not for A2/E2 (Table A4 and Table A5). Confidence intervals included fewer cumulative measured values under eCO2 than under aCO2 treatment. The proportions ranged from 94% to 44% at block A1/E1, from 79% to 65% at A2/E2, and from 70% to 69% at A3/E3. Outliers almost exclusively occurred before 2004, with the exception of E1, where the confidence interval was below the measurements until 2007. For both aCO2 and eCO2, all outliers were close to the upper edge of the confidence range, indicating an underestimation of LandscapeDNDC during the first half of the simulation period. No deviations occurred after 2007. Average RMSE values increased under eCO2, with a moderate increase from A1 to E1 (about 20%) and a slight increase from A3 to E3 (about 9%). However, RMSE (Table A3) at E2 remained virtually unchanged compared to A2 (increase of about 1%).

Cumulative N 2 O Emissions
The measured N 2 O emissions for eCO 2 increased significantly compared to aCO 2 at all blocks (Table A4), while the associated confidence intervals of the LandscapeDNDC simulations remained largely unchanged (Figure 4). For aCO 2 plots, confidence intervals included 39.2% (A1), 27.8% (A2), and 38.3% (A3) of the cumulative measured N 2 O emissions, thereby underestimating the measurements, especially in the early years up to 2007/08. A very contrasting picture appeared for eCO 2 , where confidence intervals were completely underestimating measurements, including none of the cumulative measured values for E1, E2, and E3. The extent of the underestimation depended strongly on the plot under consideration. If we compared the last cumulative values of measurements and the upper limit of the confidence intervals, we found a relative deviation of only 9% for E2 but of 282% for E1. For E1 in particular, a sequence of several sudden increases in cumulative N 2 O emissions in the years 1998-2001 could be observed. Consequently, the RMSE (Table A3) of N 2 O emissions increased for all eCO 2 plots but much more for E1 (+190% compared to A1) than for E2 (+28%) and E3 (+57%).

Cumulative N2O Emissions
The measured N2O emissions for eCO2 increased significantly compared to aCO2 at all blocks (Table A4), while the associated confidence intervals of the LandscapeDNDC simulations remained largely unchanged (Figure 4). For aCO2 plots, confidence intervals included 39.2% (A1), 27.8% (A2), and 38.3% (A3) of the cumulative measured N2O emissions, thereby underestimating the measurements, especially in the early years up to 2007/08. A very contrasting picture appeared for eCO2, where confidence intervals were completely underestimating measurements, including none of the cumulative measured values for E1, E2, and E3. The extent of the underestimation depended strongly on the plot under consideration. If we compared the last cumulative values of measurements and the upper limit of the confidence intervals, we found a relative deviation of only 9% for E2 but of 282% for E1. For E1 in particular, a sequence of several sudden increases in cumulative N2O emissions in the years 1998-2001 could be observed. Consequently, the RMSE (Table A3) of N2O emissions increased for all eCO2 plots but much more for E1 (+190% compared to A1) than for E2 (+28%) and E3 (+57%).

Discussion
The study presented here aimed at simulating long-term effects of atmospheric CO2 enrichment on various environmental target variables of a grassland ecosystem. The CO2 fumigation was performed as part of a long-term FACE experiment, from which the measured data of plant biomass, SWC, CO2, and N2O emissions collected continuously for more than 13 years were taken and used for calibration and validation. With the exception of N2O emissions, it was possible to satisfactorily simulate the long-term effects of the CO2 enrichment for all target variables.

Biomass
Agreement between simulation and measurement of biomass, especially with regard to the eCO2 effect, varied depending on the location. For block A1/E1, agreement with measured values remained essentially the same, suggesting that the eCO2-induced increase in biomass production was correctly simulated by the model, although biomass production itself was slightly underestimated. The opposite was observed for A2/E2, where the agreement between confidence

Discussion
The study presented here aimed at simulating long-term effects of atmospheric CO 2 enrichment on various environmental target variables of a grassland ecosystem. The CO 2 fumigation was performed as part of a long-term FACE experiment, from which the measured data of plant biomass, SWC, CO 2 , and N 2 O emissions collected continuously for more than 13 years were taken and used for calibration and validation. With the exception of N 2 O emissions, it was possible to satisfactorily simulate the long-term effects of the CO 2 enrichment for all target variables.

Biomass
Agreement between simulation and measurement of biomass, especially with regard to the eCO 2 effect, varied depending on the location. For block A1/E1, agreement with measured values remained essentially the same, suggesting that the eCO 2 -induced increase in biomass production was correctly simulated by the model, although biomass production itself was slightly underestimated. The opposite was observed for A2/E2, where the agreement between confidence intervals and measured values was almost perfect for both A2 and E2, although the simulations showed an increase due to elevated CO not given by the data. In context, this indicated (at least) two growth factors that were not correctly Agronomy 2020, 10, 50 9 of 17 captured in the LandscapeDNDC setup used. One of these factors strengthened the CO 2 -independent growth for A1/E1, while the other factor negated the CO 2 growth effect for A2/E2.
The eCO 2 reaction of the biomass differentiated according to the blocks could thus be assumed to be closely related to the respective soil hydrological conditions, which were very different for A1/E1 and A2/E2 with regard to both the mean groundwater levels and the SWC values. Previous research by Andresen et al. [44] found significant correlations of biomass with respect to CO 2 treatment and SWC on this study site, explicitly leaving open whether, in this case, a higher biomass was caused by higher SWC or vice versa. Kellner et al. [25] used a coupled hydrological plant model with physics-based soil hydraulics and performed multivariate simulations over the same period as this study. They obtained a RMSE of 1400-1500 kg DM ha −1 (dry matter per hectare), which is higher than the corresponding RMSE values in this study (1000-1250 kg DM ha −1 ). We argue that this was due to a major difference in the model setups, since Kellner et al. did not incorporate a possible N limitation for plant growth. This assumption is backed up by a previous study using an N-limited model approach, which found a massive drop in simulated biomass production if N uptake from the groundwater was ignored [26]. The same process of N uptake was also used in this study, showing that biomass production at the field site depends on groundwater-borne N supply, reflecting more N limited conditions at the driest block A1/E1 compared to the wettest block A2/E2.

SWC
Agreement between SWC simulations and measurements was satisfactory in terms of RMSE (6.2-7.6%), which is comparable to results by Kellner et al. [25] who achieved an RMSE of 6-10% using a physically-based hydrological modeling framework for the same period and field site. The confidence intervals of the simulations did not capture most of the observations but were much narrower than the fluctuation range of the measurement data. Furthermore, simulated SWC values followed the dynamic development of the observed SWC for both the annual cycle and most of the short term fluctuations. The recurrent underestimation of observed SWC peaks in winter was presumably due to the lower saturation level in the simulations, although it is not possible to clearly state to what extent this was due to the model setup or measurement errors (e.g., measured SWC values were partly higher than the pore volume and had to be cut off). Despite this, simulations captured the mean effect of CO 2 fumigation and were qualitatively correct at all blocks, even though measured SWC at A1/E1 and A2/E2 showed opposite eCO 2 reactions, while at A3/E3, almost no change occurred on average.
The reason that the eCO 2 effect turned from an increase at the driest plot E1 to a decrease at the wettest plot E2 could have been the more frequent exceedances of field capacity at E2. Niklaus et al. [45] found a similar increase of the SWC under elevated CO 2 in a grassland experiment until the effect was reversed when the field capacity was exceeded. Qi et al. [46] reported reduced transpiration and increased SWC in both simulations and observations when investigating the effect of eCO 2 on a semi-arid rangeland.

Cumulative CO 2 Emissions
The confidence intervals of the simulated cumulative CO 2 emissions generally covered the measured values well (RMSE of 199-239 mg CO 2 m −2 h −1 ). Only in the first years, the measured CO 2 emissions were partially underestimated, especially under eCO 2 . Since the difference between measured data and simulations built up, especially in the years 1998-2001, we suspected an initial adaptation effect to CO 2 fumigation as a possible cause. This agrees with results reported by Kammann [47], who showed that there was an increase in ecosystem respiration in the first two years under eCO 2 but that it declined again in the third year. Kammann deduced that a likely cause of increased respiration was either increased rhizodeposition or increased fine root turnover.
However, even long after the initialization of the experiment (e.g., in summer 2010), emission peaks occurred that were underestimated by simulations (see Figure A2), indicating that acclimatization to eCO 2 is an ongoing process and single weather events, e.g., drying and rewetting, can mask the CO 2 effect [48].

Cumulative N 2 O Emissions
The underestimation of the observed N 2 O emissions by the simulation suggested that essential processes of N 2 O formation were not correctly mapped in the model setup used. The regular underestimation of the SWC in winter, for example, could have led to an underestimation of anaerobic denitrification. However, since almost all N 2 O peaks occurred at times outside winter when SWC was not underestimated, shortcomings in peak SWC simulation could not be mainly responsible for the underestimation of N 2 O emissions (see Figures 2 and A3). This would be consistent with the previous assumption that saturated soil conditions are present in periods of high SWC, leading to complete anaerobic denitrification to N 2 instead of N 2 O. N 2 O emission peaks during periods of lower SWC that may still arise from denitrification but do not necessarily require anaerobic conditions in the GiFACE meadow soil [49]. In principle, there is also the possibility that N 2 O emissions resulted from the time-delayed aerobic denitrification of NO 3 , which reached the root zone as a result of high groundwater levels or capillary rise.
Since N 2 O emissions were highest under eCO 2 at the driest A1 plot, the question arises as to why aerobic denitrification was strongest at the plot with the lowest presumed NO 3 supply. An explanation could be that increased N 2  in the early years of CO 2 fumigation, resulted in increased turnover of soil organic matter (SOM). Root exudation [50] and fine root degradation [51] were already assumed as organic N sources for increased N 2 O emissions under eCO 2 and are possibly related to increased turnover processes due to fungal activity [31,[51][52][53].
It should be noted that neither priming effects nor fungal decomposition processes or even oxidation of organic N are currently implemented in LandscapeDNDC. If one or more of these processes is substantially involved in N 2 O production in GiFACE, this could explain the underestimation of N 2 O emissions under eCO 2 .

Conclusions
The simulations under aCO 2 showed a satisfactory agreement with measurements for all target variables, while under eCO 2 , this could be achieved for biomass, SWC, and CO 2 emissions but not for N 2 O emissions. The underlying problem is that N 2 O emissions result from a large number of processes and factors that cannot be monitored at the ecosystem level or only for very short time frames. The consequence of this is that N 2 O emission peaks are often the result of several possible causes, not all of which are necessarily known. In our case, conclusions can be drawn from the interplay of earlier investigations, long-term measurements, and simulations.
The increase in harvest biomass and CO 2 emissions under eCO 2 speaks for increased production of SOM and soil respiration. Although the increase in respiration could be attributed in part to the increased SWC under eCO 2 , this did not apply to A2/E2. Here, emissions increased for both CO 2 and N 2 O despite generally lower SWC at E2. This suggests that the measured increase in greenhouse gas emissions was largely due to the oxidation of SOM. If this was the case, it can be assumed that NO 3 from groundwater is also a rather indirect contributor to the increase in N 2 O emissions. N 2 O production could thus be explained less by anaerobic denitrification of (groundwater) NO 3 than by oxidation of labile SOM, the formation of which is forced by the combination of increased CO 2 and groundwater NO 3 . Since LandscapeDNDC is currently unable to capture this, and the simulation quality of the N 2 O emissions under eCO 2 differs radically from that of the other target variables, we consider the above explanation approach to be promising.
The example of N 2 O emissions shows that the assessment of the complex impacts of increased CO 2 benefits from the inclusion of several interrelated environmental variables. However, this also implies that weak points in the simulation, e.g., peak SWC values, can impair the simulation quality of the other target variables and must be catered to. For the further use of LandscapeDNDC in groundwater-impacted meadows, we therefore recommend the oxidation of labile organic N to be included in the next model setup. The same applies to a better implementation of the special hydrological conditions in GiFACE. In this regard, we also recommend further studies on the influence of long-term changes in groundwater levels and nitrate concentrations in groundwater. Finally, research on the mineralization of organic material by mycorrhiza could help to improve projections about greenhouse gas emissions for grasslands under future atmospheric CO 2 conditions.  We would like to further acknowledge the financial support provided by the Deutsche Forschungsgemeinschaft (DFG) (BR2238/13-1 and HO6420/1-1).
Appendix A Table A1. Measured data implemented in LandscapeDNDC: FSM = field site measurements (see Section 2.1); WD = weather data [54]; variable = soil moisture, CO 2 and N 2 O emissions measurements ranged from several per week to several per month.    Figure A1. Sensitivity analysis diagram: calculation of the most sensitive parameters where 750,000 parameter sets were sampled in 3 FAST runs, one for each plot (= measuring site). For each run, the 12 most sensitive parameters were calculated separately for 5 objective functions: RMSE of biomass, C-N ratio, CO2 emissions, N2O emissions, and soil water content. After that, the sensitive parameters were unified within each FAST run. Finally, an intersection of the unified parameters was made among the FAST runs, creating the choice of sensitive parameters that was used for calibration. Figure A1. Sensitivity analysis diagram: calculation of the most sensitive parameters where 750,000 parameter sets were sampled in 3 FAST runs, one for each plot (= measuring site). For each run, the 12 most sensitive parameters were calculated separately for 5 objective functions: RMSE of biomass, C-N ratio, CO 2 emissions, N 2 O emissions, and soil water content. After that, the sensitive parameters were unified within each FAST run. Finally, an intersection of the unified parameters was made among the FAST runs, creating the choice of sensitive parameters that was used for calibration.
parameter sets were sampled in 3 FAST runs, one for each plot (= measuring site). For each run, the 12 most sensitive parameters were calculated separately for 5 objective functions: RMSE of biomass, C-N ratio, CO2 emissions, N2O emissions, and soil water content. After that, the sensitive parameters were unified within each FAST run. Finally, an intersection of the unified parameters was made among the FAST runs, creating the choice of sensitive parameters that was used for calibration. . Figure A3. Time series of N2O emissions. Measurements are depicted in lines, and confidence intervals of simulations are depicted as colored areas.