# The Role of Respiration in Estimation of Net Carbon Cycle: Coupling Soil Carbon Dynamics and Canopy Turnover in a Novel Version of 3D-CMCC Forest Ecosystem Model

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## Abstract

**:**

_{h}) is explicitly simulated. The aim was to quantify NEE as a forward problem, by subtracting ecosystem respiration (R

_{eco}) to gross primary productivity (GPP). To do so, we developed a simplification of the soil carbon dynamics routine proposed in the DNDC (DeNitrification-DeComposition) computer simulation model. The method calculates decomposition as a function of soil moisture, temperature, state of the organic compartments, and relative abundance of microbial pools. Given the pulse dynamics of soil respiration, we introduced modifications in some of the principal constitutive relations involved in phenology and littering sub-routines. We quantified the model structure-related uncertainty in NEE, by running our training simulations over 1000 random parameter-sets extracted from parameter distributions expected from literature. 3D-CMCC-PSM predictability was tested on independent time series for 6 Fluxnet sites. The model resulted in daily and monthly estimations highly consistent with the observed time series. It showed lower predictability in Mediterranean ecosystems, suggesting that it may need further improvements in addressing evapotranspiration and water dynamics.

## 1. Introduction

_{2}), have pushed research efforts to better investigate biogeochemical carbon (C) flux dynamics and patterns between atmosphere and biosphere, and to upscale C flux estimates from site-specific to regional, continental and global scales. Increased atmospheric concentration of CO

_{2}, combined with increasing temperatures and size variations of ecosystem C pools, are responsible for year-to-year terrestrial ecosystem carbon flux perturbations, through the variation of both photosynthetic and respiration rates [1].

_{eco}) and gross primary productivity (GPP). Thus, flux partitioning algorithms have been developed to partition eddy covariance NEE into photosynthetic uptake and respiratory release [5,6]. At the same time, EC flux measurements provide key information for the parameterization, calibration and validation of process-based forest ecosystem models (FEMs) contributing to large-scale estimates of main ecosystem C pools.

_{eco}estimation [18,19]. Hence, the implementation of biogeochemical models integrating soil respiration models and FSTMs is a great opportunity to reliably estimate NEE [20,21,22].

_{h}) was explicitly simulated to dynamically quantify stock changes of 7 different SOC pools mediated by the amount of active microbial C pools, following the rationale proposed in the DNDC [38]. The aim of this study was to: (1) test the performance of the modified model version, comparing model NEE estimates against independent time series for 6 Fluxnet sites, representing different forests in different climatic areas, distributed over a wide latitudinal gradient amongst European EC sites; and (2) quantify uncertainty associated to 3D-CMCC-PSM constitutive relations structure and parameterization.

## 2. Materials and Methods

#### 2.1. Study Area and Data

_{eco}) were partitioned using [6]. Information about forest structure and total SOC at the beginning of the simulation was collected from literature (e.g., [41,42]) and PIs information. Sites were chosen to represent 3 diverse forest ecosystems, dominated by different species composition from deciduous broadleaved, DBF (i.e., Fagus sylvatica L.), evergreen broadleaved, EBF (i.e., Quercus ilex L.), and evergreen needle leaved, ENF (i.e., Pinus sylvestris L. and Picea abies L.), representing the most common European forest species from boreal to mediterranean ecoregions across Europe.

#### 2.2. Model Description

_{A}) was explicitly modeled as the sum of growth and maintenance respiration (R

_{G}and R

_{M}, respectively). The first was computed as a fixed ratio of new growth tissues (30%) and the latter was based on nitrogen content in stems, branches, leaves, fine and coarse roots, non-structural carbon (NSC), and fruit tree pools. carbon allocation among these pools was controlled by species-specific parameters, phenology, light and water availability. Water cycle was modeled calculating the daily balance between precipitation, canopy transpiration, evaporation, soil evaporation, and runoff. Meteorological variables used to force the model were: global solar radiation (MJ m

^{−2}day

^{−1}), maximum and minimum air temperature (°C), relative humidity (%), and daily cumulated precipitation (mm day

^{−1}). To be initialized, the model required knowing stand structural characteristics such as: species composition, stand density, diameter at breast height (DBH), tree height and age. Soil initialization required the estimation of total organic carbon (TOC) in the different SOC pools, as described in the following section.

#### 2.3. Model Improvements

#### 2.3.1. Soil Carbon Dynamics

_{2}, partly stored into microbial metabolic biomass (labile), partly in structural microbial biomass (resistant), and partly transformed in other organic compounds [38]. SOC stability is also related to its chemical recalcitrance, its accessibility, and interaction with clays [45]. We divided SOC in 7 pools to take these differences into account. Humic pool (we use the term Humads, to be consistent with [38]) was divided into a more labile (labile Humads, which stands for Humic acid), an intermediate (resistant Humads, which stands for Fulvic acid) and a more resistant sub-pool (Humus, which stands for Humine).

_{2}efflux produced from a specific C pool decomposition,${C}_{L}\left(t\right)$ is the amount of new carbon entering the specific soil pool, ${C}_{p}\left(t\right)$the amount of carbon in that C pool, $B\left(t\right)$ the microbial biomass competing for $C\left(t\right)$. α and β respectively represented the microbial turnover and SOM consumption factors. α was treated as a constant value, as in [38].

#### 2.3.2. Deciduous Phenology

^{2}m

^{−2}).

_{0}, and the last day of budburst ($B{B}_{T}$) the last possible one to reach complete leaf development, the domain of the function was [t

_{0}, $B{B}_{T}$]:

_{Max}is the maximum temperature at which senescence is effective, $T\left(t\right)$ is daily average Temperature, ${D}_{L}\left(0\right)$ photoperiod at the first day of senescence, ${D}_{L}\left(t\right)$ photoperiod at the ith day of the year.

#### 2.3.3. Evergreen Phenology

^{−2}day

^{−1}(i.e., resource that leaves are competing for), and the light needed to survive in a progressively more shaded canopy. ${r}_{i}$ is the max photosynthetic rate (gC m

^{−2}day

^{−1}), ${m}_{i}$ is the maintenance respiration in gC m

^{−2}day

^{−1}, ${Y}_{i}$ carbon yield.

- (1)
- Older leaves live in the shaded portions of the canopy, where light transmitted is reduced following Lambert Beer’s exponential decay equation. For this reason, we expect an exponential reduction in absorbed photosynthetically active radiation (APAR);
- (2)
- An age dependent quasi-exponential decay in leaf quantum yield efficiency [53]. These decays impact on the reduction of ${r}_{i}$;
- (3)
- Nitrogen content in older leaves is often lower than in young ones, because of its transfer from portions of the crown with low productivity to portions more exposed to light [53]. Since maintenance respiration is proportional to nitrogen content, we expect an exponential reduction in ${m}_{i}$;
- (4)
- ${Y}_{i}$ was assumed to be constant as in [17] because of the joint effect of reduction in respiration rate and quantum yield efficiency.

_{(i)}) as the inversion of R* (i.e., ${S}_{i}^{*}\left(t\right)$). We simplified the theoretical model using the following function:

#### 2.3.4. Production of Fresh Organic Matter

#### 2.3.5. Optimization

^{obs}represents EC daily NEE, Y

^{sim}Modeled NEE for the same day, ${\overline{Y}}^{obs}$ the average EC daily NEE over the train time series. The first part of the RHS of the equation represents the square of the Pearson Correlation coefficient (R), the second the Nash-Sutcliffe Efficiency index (NSE).

#### 2.3.6. Validation Analysis

## 3. Results

#### 3.1. Evaluation of Daily, Seasonal, and Annual NEE Estimations

^{−2}day

^{−1}. MAE ranged between 0.96 and 1.78 gC m

^{−2}day

^{−1}, and on average it decreased almost twice on monthly timescale. MAB showed similar behavior for DBF and ENF. It ranged between 0.39 and 0.56 gC m

^{−2}day

^{−1}(0.50 on average) for daily time series. Mediterranean forests resulted as the ones with highest MAB, and showed no significant reduction when predictions were aggregated on monthly scale. Differently from the other simulations, even NSE just improved slightly for ITCpz, and even reduced for FRPue simulation.

^{−2}day

^{−1}), one value per season, for the duration of the test dataset. Seasonal aggregations showed that 3D-CMCC-PSM poorly performed in predicting seasonal fluxes. NSE was generally negative in summer, with the exclusion of DEHai and FRPue. 3D-CMCC-PSM generally best reproduced NEE dynamics in fall (R ranging between 0.22 and 0.89). ENF ecosystems showed consistently higher correlation in spring predictions, with R of 0.65 and MAB of 0.62 gC m

^{−2}day

^{−1}on average. In the case of evergreen stands, 3D-CMCC-PSM consistently showed poor performance in summer. Expectedly, DBF performed the worst in winter (Table 5). NSE on average resulted positive only in fall for both DBF and EBF, and spring, for ENF stands.

#### 3.2. Anomalies and Parameters Related Uncertainty

^{−2}day

^{−1}. Highest difference in magnitude occurred in ITCol (difference in residuals higher than 0.5 gC m

^{−2}day

^{−1}in 5 years out of 12). Highest difference was shown in ITCpz, where the sign was correctly reproduced only once out of 8 years, and having more than 1gC m

^{−2}day

^{−1}of residual difference (Figure 7).

#### 3.3. Comparison with the 5.1 Version of 3D-CMCC-FEM

#### 3.4. Daily and Monthly Reco

^{−2}day

^{−1}on average, and MEB ranged between 0.43 and 1 gC m

^{−2}day

^{−1}. Most of the bias happened in summer, where Reco was generally overestimated, especially in ITCol and ITCpz. NSE was positive in any case but ITCpz. It was generally lower in DBF (0.32), and higher in FIHyy (0.60) and FRPue (0.57). Reco at monthly timescale was strongly improved in predictability, especially for ITRen, whose R increased to 0.67 and NSE to 0.84. Monthly predictions showed improvements for the other simulations too, improving R and NSE of about 0.07. Since most of the bias occurred in summer, monthly predictions showed no dramatic improvements for neither RMSE nor MEB. The only exception was ITRen, whose RMSE reduced of about 0.4 gC m

^{−2}day

^{−1}, suggesting that daily Reco may be noisy.

## 4. Discussion

#### 4.1. 3D-CMCC-PSM Predictability in Estimating NEE

_{eco}and GPP [63], which positively affects correlation between EC data and MD results.

_{eco}. The scarcity of the model in representing EBF C fluxes was especially attributable to GPP predictions. 3D-CMCC-PSM and 3D-CMCC FEM inability in predicting GPP in ITCpz and FRPue sites, denoted the necessity to better represent the relations between Mediterranean forests and environmental factors [64,65]. In FRPue the model well reproduced spring, summer and fall NEE. On the other hand, it showed a bias of around 1 gC m

^{−2}day

^{−1}in winter, suggesting it was missing some particularly important seasonal processes. For example, evergreen phenology still didn’t consider secondary or continuous growth. Thus, species like Quercus ilex, which exhibit secondary gem sprouts in fall [54], have fresh leaves and mild temperatures to guarantee photosynthetic activity in fall and winter, partly explaining 3D-CMCC FEM and 3D-CMCC-PSM systematic underestimation.

_{eco}still needs to be improved. Especially for DBF sites (e.g., DEHai), winter R

_{eco}was mostly driven by RH. RH was exponentially affected by soil temperatures and especially moisture [69], which are calculated by the model, and could be over-fluctuating in winter. Moreover, EC data are prone to random noise [70], whose relative impact on performance metrics may be relatively larger.

^{−2}day

^{−1}higher than RH, suggesting that it may be the principal driver of biased summer Reco (Figure 9). This misbehavior may be related to the method used to estimate maintenance respiration, an exponential relationship between respiration, moisture, and temperature [61].

_{2}, and living space, with no depth limitation.

#### 4.2. 3D-CMCC-PSM Uncertainty in Estimating NEE

_{eco}estimation was affected by the cascade of uncertainties related to the calculation of R

_{A}and heterotrophic respiration, calculated independently. R

_{A}routine may strongly be influenced by uncertainties in R

_{M}estimation, which often resulted in R

_{A}overestimation. The R

_{M}was in fact simulated by a set of empirical relations, which involve the use of a fixed non-acclimating Q10 factor, whose generality is known to be inaccurate [84]. Moreover, the rationale of Ryan’s R

_{M}calculation [85] is affected by uncertainty in estimating daily increment of N pools, generally estimated by forest ecosystem models as a fixed proportion of daily C increment.

## 5. Conclusions

_{eco}dynamics in a forest ecosystem, especially in scaling up daily results to monthly NEE averages. We think that 3D-CMCC-PSM is a solid basis to further explore the effects of soil structure on carbon and Water dynamics, especially in Mediterranean systems, and be used as a tool for predicting forest growth and ecosystem services, and address questions related to future scenario forecasting.

## Supplementary Materials

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**Figure 2.**3D-CMCC version of PSM main flowchart modified from [36]. Red-circled boxes represent the pools and variables introduced or modified by 3D-CMCC-PSM.

**Figure 3.**Soil Carbon dynamics in 3D-CMCC-PSM. The three macro pools are highlighted by red boxes (dead C pool) and blue box (live C pool, i.e., microbial). Blue-filled boxes represent the processes simulated by the soil model.

**Figure 4.**Graphic representation of the C tradeoff function. The axes represent respectively bud burst days (BB

_{T}), vegetative days (Veg_day), and the fraction of total NSC invested in leaf development ($\frac{\partial NSC}{\partial t})$. The shorter BB

_{T}, the higher the maximum NSC fraction.

**Figure 5.**Taylor diagrams representing 3D-CMCC-PSM performance in (

**a**) daily, (

**b**) monthly and (

**c**) annual NEE estimation for the test-set. ITCol and DEHai represent DBF (red and green dots); ITRen and FIHyy ENF (blue and turquoise). ITCpz and FRPue represent EBF (yellow and magenta dots). The closest a simulation lied to the “Ref” point, the better 3D-CMCC-PSM represented NEE patterns. X- and Y- axes represent NEE standard deviation (SD): the closest to 1, the better the performance. Simulations with R ≥ 0.9, and difference in SD with EC NEE less than 0.5 5 gC m

^{−2}day

^{−1}showed very good performance. Simulations with 0.75 ≤ R < 0.9, and difference in SD with EC NEE between 0.5 and 1 gC m

^{−2}day

^{−1}showed good performance. Simulations with 0.35 ≤ R < 0.75, and difference in SD with EC NEE between 1 and 1.5 gC m

^{−2}day

^{−1}showed sufficiently good performance.

**Figure 6.**Model structure-related uncertainty in estimating NEE (gC m

^{−2}day

^{−1}) per DoS (Day of Simulation) by a random choice of parameter values from prior distributions. Data represent 300 1-year simulations from randomly extracted parameterization-sets. Average daily simulations (black lines) and standard deviation (grey areas). Red dotted lines represent daily NEE simulation for the optimized parameterization set (Table S2). First column represents DBF sites, the second ENF, the third EBF.

**Figure 7.**Inter-annual variability (IAV) for the test time-series (gC m

^{−2}month

^{−1}). Observed IAV in gray boxes, simulated IAV in orange. First column represents DBF sites, the second ENF, the third EBF.

**Figure 8.**Patterns in daily NEE (gC m

^{−2}day

^{−1}) per DoY (Day of Year) calculated from test-sets on a site level. Observed EC average patterns (black dotted line) and standard deviation (gray area). Simulated average patterns (red dotted line) and standard deviation (orange area). First column represents DBF sites, the second ENF, the third EBF.

**Figure 9.**Patterns in daily soil respiration (SR) heterotrophic and autotrophic components (gC m

^{−2}day

^{−1}). Patterns per DoY (Day of Year) calculated from test-sets on a site level. Microbial respiration average patterns (black-dotted lines) and standard deviation (gray areas). Autotrophic root respiration average patterns (red-dotted lines) and standard deviation (orange areas). First column represents DBF sites, the second ENF, the third EBF.

**Table 1.**Site descriptions and stand initialization data. Mean temperature (T) and Precipitation (prec.) are annual averages collected at site.

Site Name | Coords (Lat°/Lon°) | Climate | Species Composition | Mean Annual T (°C) | Mean Annual Prec. (mm year^{−1}) | Mean DBH (cm) | Tree Height (m) | Stand Age (years) | Stand Density (trees ha^{−1}) |
---|---|---|---|---|---|---|---|---|---|

Collelongo (ITCol) | 41.8/13.5 | Temperate | Fagus sylvatica L. (DBF) | 6.3 | 1180 | 20.27 | 19.84 | 100 | 900 |

Hainich (DEHai) | 51.0/10.4 | Temperate | Fagus sylvatica L. (DBF) | 8.3 | 720 | 30.8 | 23.1 | 120 | 334 |

Hyytiälä (FIHyy) | 61.8/24.2 | Boreal | Pinus sylvestris L. (ENF) | 3.8 | 709 | 10.3 | 6.5 | 39 | 1796 |

Renon (ITRen) | 46.5/11.4 | Temperate | Picea abies L. (ENF) | 4.7 | 809 | 16.98 | 11.32 | 50 | 767 |

Castelporziano (ITCpz) | 41.7/12.3 | Mediterranean | Quercus ilex L. (EBF) | 15.6 | 780 | 16 | 12.5 | 45 | 458 |

Puechabon (FRPue) | 43.7/3.6 | Mediterranean | Quercus ilex L. (EBF) | 13.5 | 883 | 7 | 6 | 59 | 6149 |

**Table 2.**Description of the different phenological phases for deciduous and evergreen species used in 3D-CMCC PSM.

Deciduous | Evergreen | ||
---|---|---|---|

Phase | Trigger | Phase | Trigger |

Bud Burst | GDD threshold | Bud Burst | GDD treshold |

Leaf Development | PeakLai/2 | PeakLai | Pipe Model |

PeakLai | Pipe Model | Leaffall | Daylength Treshold |

Leaffall | Daylength Treshold | ||

Unvegetative | [49] |

Statistics | Formulation | Use and Ranges |
---|---|---|

Pearson Coefficient | $r=\frac{{{\displaystyle \sum}}_{i}^{n}{Y}_{i}^{obs}\xb7{Y}_{i}^{sim}-\frac{{{\displaystyle \sum}}^{\text{}}{Y}_{i}^{obs}\xb7{{\displaystyle \sum}}^{\text{}}{Y}_{i}^{sim}}{n}}{\sqrt{{{\displaystyle \sum}}^{\text{}}{\left({Y}_{i}^{obs}\right)}^{2}-\frac{{\left({{\displaystyle \sum}}^{\text{}}{Y}_{i}^{obs}\right)}^{2}}{n}}\sqrt{{{\displaystyle \sum}}^{\text{}}{\left({Y}_{i}^{sim}\right)}^{2}-\frac{{\left({{\displaystyle \sum}}^{\text{}}{Y}_{i}^{sim}\right)}^{2}}{n}}}$ | Estimation of model’s measure of correlation with EC data [0;1] |

Nash Sutcliffe efficiency | $NSE=1-\frac{{{\displaystyle \sum}}_{i}^{n}{\left({Y}_{i}^{obs}-{Y}_{i}^{sim}\right)}^{2}}{{{\displaystyle \sum}}_{i}^{n}{\left({Y}_{i}^{obs}-{\overline{Y}}^{obs}\right)}^{2}}$ | Estimation of model’s predictability [−∞;1] |

Root Mean Square Error | $RMSE=\sqrt{\frac{{{\displaystyle \sum}}_{i}^{n}{\left({Y}_{i}^{obs}-{Y}_{i}^{sim}\right)}^{2}}{n}}$ | Estimation of model’s accuracy gC m^{−2} day^{−1} [0; ∞] |

Mean Absolute Bias | $MAB=\frac{1}{n}{{\displaystyle \sum}}_{i}^{n}\frac{\left|{Y}_{i}^{obs}-{Y}_{i}^{sim}\right|}{\sigma \left({Y}_{i}^{obs}\right)}$ | Estimation of model’s bias gC m^{−2} day^{−1} [0; ∞] |

**Table 4.**Daily and Monthly Validation statistics calculated on the test-set. As stated in Table 3, R and NSE are dimensionless; RMSE and MAB are gC m

^{−2}day

^{−1}.

DEHai | ITCol | FIHyy | ITRen | ITCpz | FRPue | Mean | |
---|---|---|---|---|---|---|---|

Daily NEE | |||||||

R | 0.84 | 0.76 | 0.67 | 0.65 | 0.24 | 0.65 | 0.64 |

NSE | 0.67 | 0.5 | 0.34 | 0.21 | -0.26 | 0.35 | 0.3 |

RMSE | 1.84 | 2.7 | 1.48 | 2.32 | 1.8 | 1.39 | 1.92 |

MAB | 0.39 | 0.5 | 0.53 | 0.56 | 1.15 | 0.76 | 0.65 |

Monthly NEE | |||||||

R | 0.93 | 0.92 | 0.96 | 0.97 | 0.42 | 0.59 | 0.8 |

NSE | 0.81 | 0.76 | 0.9 | 0.87 | 0.12 | 0.2 | 0.61 |

RMSE | 1.15 | 1.58 | 0.45 | 0.72 | 1.24 | 1 | 1.02 |

MAB | 0.28 | 0.32 | 0.21 | 0.24 | 1.25 | 0.86 | 0.53 |

**Table 5.**Seasonal validation statistics calculated on the test-sets and aggregated by ecosystem type. As stated in Table 3 and Table 4, R and NSE are dimensionless; RMSE and MAB are gC m

^{−2}day

^{−1}. MAM stands for March, April, and May. JJA stands for June, July, and August. SON stands for September, October, and November. DJF stands for December, January, and February. DBF stands for Deciduous Broadleaf Forests, EBF for Evergreen Broadleaf Forests, ENF for Evergreen Needle leaf Forests.

R | NSE | RMSE | MAB | |
---|---|---|---|---|

DBF | ||||

MAM | 0.43 | −0.36 | 2.77 | 0.72 |

JJA | 0.36 | −0.02 | 2.96 | 1.17 |

SON | 0.82 | 0.58 | 1.9 | 0.58 |

DJF | 0.2 | −0.93 | 0.7 | 1.58 |

EBF | ||||

MAM | 0.65 | 0.28 | 1.83 | 0.62 |

JJA | 0.11 | −0.92 | 2.76 | 1.01 |

SON | 0.51 | 0.03 | 1.56 | 0.74 |

DJF | 0.38 | −1.59 | 0.51 | 0.82 |

ENF | ||||

MAM | 0.18 | −0.41 | 1.95 | 1.2 |

JJA | 0.32 | −0.34 | 1.58 | 1 |

SON | 0.45 | −0.19 | 1.3 | 0.79 |

DJF | 0.47 | −6.13 | 1.45 | 1.4 |

**Table 6.**Comparisons between 3D-CMCC-FEM 5.1 (5.1) and 3D-CMCC-PSM (PSM) versions, using the same parameterization for 4 out of 6 sites (ITRen, FRPue, DEHai, FIHyy). As stated in Table 3, R and NSE are dimensionless; RMSE is gC m

^{−2}day

^{−1}. Bold values represent best performing version.

Version | ITRen | FRPue | DEHai | FIHyy | Avg | |
---|---|---|---|---|---|---|

Daily | 5.1 | 0.81 | 0.82 | 0.92 | 0.91 | 0.87 |

R | PSM | 0.88 | 0.64 | 0.93 | 0.91 | 0.85 |

Monthly | 5.1 | 0.95 | 0.64 | 0.97 | 0.96 | 0.89 |

R | PSM | 0.96 | 0.84 | 0.98 | 0.96 | 0.94 |

Daily | 5.1 | 0.61 | −0.54 | 0.84 | 0.87 | 0.45 |

NSE | PSM | 0.72 | 0.09 | 0.96 | 0.76 | 0.63 |

Monthly | 5.1 | 0.91 | −0.11 | 0.94 | 0.91 | 0.66 |

NSE | PSM | 0.91 | 0.56 | 0.98 | 0.92 | 0.84 |

Daily | 5.1 | 2.09 | 1.52 | 1.85 | 1.56 | 1.76 |

RMSE | PSM | 1.59 | 1.96 | 1.91 | 1.57 | 1.76 |

Monthly | 5.1 | 0.97 | 1.01 | 1.07 | 0.91 | 0.99 |

RMSE | PSM | 0.82 | 1.09 | 0.82 | 0.93 | 0.92 |

**Table 7.**Daily and Monthly Validation statistics for R

_{eco}calculated on the test-set. As stated in Table 3, R and NSE are dimensionless; RMSE and MAB are gC m

^{−2}day

^{−1}.

DEHai | ITCol | FIHyy | ITRen | ITCpz | FRPue | Mean | |
---|---|---|---|---|---|---|---|

Daily Reco | |||||||

R | 0.79 | 0.71 | 0.90 | 0.67 | 0.45 | 0.86 | 0.73 |

NSE | 0.32 | 0.32 | 0.60 | 0.34 | −0.43 | 0.57 | 0.29 |

RMSE | 1.29 | 1.83 | 1.18 | 1.03 | 1.65 | 0.70 | 1.28 |

MEB | 0.63 | 1.00 | 0.43 | 0.62 | 0.98 | 0.48 | 0.69 |

Monthly Reco | |||||||

R | 0.86 | 0.79 | 0.96 | 0.84 | 0.54 | 0.93 | 0.82 |

NSE | 0.40 | 0.37 | 0.66 | 0.69 | −0.40 | 0.67 | 0.40 |

RMSE | 1.10 | 1.67 | 1.04 | 0.64 | 1.49 | 0.54 | 1.08 |

MEB | 0.60 | 0.99 | 0.41 | 0.50 | 1.04 | 0.43 | 0.66 |

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## Share and Cite

**MDPI and ACS Style**

Marconi, S.; Chiti, T.; Nolè, A.; Valentini, R.; Collalti, A.
The Role of Respiration in Estimation of Net Carbon Cycle: Coupling Soil Carbon Dynamics and Canopy Turnover in a Novel Version of 3D-CMCC Forest Ecosystem Model. *Forests* **2017**, *8*, 220.
https://doi.org/10.3390/f8060220

**AMA Style**

Marconi S, Chiti T, Nolè A, Valentini R, Collalti A.
The Role of Respiration in Estimation of Net Carbon Cycle: Coupling Soil Carbon Dynamics and Canopy Turnover in a Novel Version of 3D-CMCC Forest Ecosystem Model. *Forests*. 2017; 8(6):220.
https://doi.org/10.3390/f8060220

**Chicago/Turabian Style**

Marconi, Sergio, Tommaso Chiti, Angelo Nolè, Riccardo Valentini, and Alessio Collalti.
2017. "The Role of Respiration in Estimation of Net Carbon Cycle: Coupling Soil Carbon Dynamics and Canopy Turnover in a Novel Version of 3D-CMCC Forest Ecosystem Model" *Forests* 8, no. 6: 220.
https://doi.org/10.3390/f8060220