Comparing soil inventory with modelling: Carbon balance in central European forest soils varies among forest types
Graphical abstract
Introduction
Globally, forests are a carbon sink (Pan et al., 2011) that needs to be maintained and strengthened to compensate for anthropogenic greenhouse gas emissions and to mitigate climate change. Forest soils have an important role in the global carbon cycle and they contain more carbon than the terrestrial biomass pool or the atmosphere (Batjes, 1996). Consequently, even small changes in the soil carbon pool can have a substantial impact on the carbon sink (Paustian et al., 2016).
In the United Nations Convention on Climate Change (UNFCCC), countries have agreed to report the annual carbon stock changes of soils under different land uses and under land-use change as a part of their greenhouse gas inventories (IPCC, 2006). A straightforward method to fulfil this commitment of the UNFCCC is a repeated large-scale soil inventory. This method was applied in Germany, where carbon stock changes were estimated by the National Forest Soil Inventory (NFSI) (Wellbrock et al., 2016). Repetition of the NFSI in Germany revealed that forest soils act with 0.39 Mg C ha−1 a−1 as a considerable carbon sink between 1990 and 2008 (Grüneberg et al., 2014). Earlier studies deliver results from soil inventories for other European countries. The measured soil carbon stock changes may vary from carbon losses in England and Wales (Bellamy et al., 2005) to no significant changes in Denmark (Callesen et al., 2015) or with 0.12 Mg C ha−1 a−1 slight increases in Finland (Rantakari et al., 2012), and with 0.35 Mg C ha−1 a−1 up to large soil carbon accumulation in France (Jonard et al., 2017).
The NFSI measurements provide information on the current soil carbon stocks but are not able to predict soil carbon changes under changing climate. In the context of the German greenhouse gas reporting system a near-future scenario of soil carbon stock development is necessary to calculate the Forest Management Reference Level (IPCC, 2014). This serves as baseline, against which the net emissions and removals reported for Forest Management, will be compared for accounting purposes. On the basis of the NFSI data, soil carbon changes can be reported only by extrapolating until the NFSI is repeated again, which is planned for 2022. The repetition of the NFSI shows changes in soil carbon stocks only in longer time periods due to the slow turnover rate of soil carbon stocks and measurement uncertainties. Calculating soil carbon stocks requires the determination of soil carbon concentrations, bulk densities, stone contents and soil depth, which all vary depending on site and have associated different measurement errors (Schrumpf et al., 2011). Due to the uncertainties of a soil inventory, the annual change rates need to be relatively high to be detected; for example, the Danish inventory reveals that annual changes must exceed 0.15 Mg C ha−1 a−1 to be detected by the inventory (Callesen et al., 2015). Furthermore, inventories allow the impact of environmental conditions as well as different management practices to be addressed only if these were surveyed during the inventory. Consequently, investigations that go beyond the conditions of the inventory or projections of soil carbon development can be made only by extrapolation. In contrast, soil carbon models allow the investigation of the effect of changing climate in combination with different management practices on soil carbon dynamics under various scenarios.
Upscaling soil organic matter (SOM) cycling in a model from fine-scale processes to soil profile or landscape level is challenging (Schmidt et al., 2011). As models which are applied on higher spatial scales usually operate with average environmental conditions, their results could be biased due to fine-scale heterogeneity. In practice, a model should be validated with measured data to assess whether it is able to predict soil carbon stocks and their changes across the entire region of interest. On a global scale, comparisons of soil organic carbon (SOC) stock predictions by various earth-system models have resulted in high variation and deviations from global SOC databases (Todd-Brown et al., 2013). On a regional scale, a comparison among models revealed that soil fertility affects the relationship between modelled SOC stocks and measured SOC stocks (Tupek et al., 2016). The lack of conformance on nutrient rich sites in that study was attributed to the lack of mechanisms that model mycorrhizal organic uptake and organo-mineral stabilisation processes. There are even fewer studies that compare modelled SOC stock changes with measurements. Studies from Finland and Sweden comparing modelled SOC stock changes and large scale inventories show SOC changes of similar order of magnitude (Rantakari et al., 2012; Ortiz et al., 2013). However, previous studies comparing model and measured estimates of soil carbon stock changes have not investigated the model performance for different forest types in detail. Soil carbon stocks and their changes are related to forest types because on the one hand the distribution of forest types depends on site properties like climate, soil texture and soil fertility, which influence soil carbon balance (Guckland et al., 2009). On the other hand tree species composition and diversity influence soil carbon balance in various ways. Species-specific properties in rooting patterns (Finér et al., 2017), litter-chemistry (Vesterdal et al., 2008) and tree architecture (Joly et al., 2017) influence litter input, litter decomposition and carbon stabilisation (Schleuß et al., 2014). Species diversity may lead to increased ecosystem productivity by niche partitioning, which increase litter input, e.g. inter-specific competition increases fine root biomass (Bolte and Villanueva, 2006). Consequently, SOC stocks are related to tree diversity (Gamfeldt et al., 2013). Therefore, the stratification of a large scale soil inventory into forest types and the comparison of measured and modelled SOC balances will provide a reference for future research of soil carbon dynamics.
We used the Yasso litter and soil carbon model in our study. This model is a widely used litter and soil carbon model (Tuomi et al., 2008; Tuomi et al., 2009; Tuomi et al., 2011a; Järvenpää et al., 2018). On average, Yasso has proven to perform well in various comparisons among models (Lehtonen et al., 2016) or in litter decomposition studies (Didion et al., 2016). It has been applied in different European countries (Thürig et al., 2005; Liski et al., 2006; Rantakari et al., 2012; Ortiz et al., 2013; Dalsgaard et al., 2016; Hernández et al., 2017), used in several national greenhouse gas inventories (www.en.ilmatieteenlaitos.fi/yasso) and has been implemented in the land surface scheme of the MPI Earth System Model (Goll et al., 2015, Goll et al., 2017). The Yasso15 is a new version of the model that is parameterized over a wide range of site conditions and was recently validated with litter-bag decomposition studies (Didion et al., 2016).
The aim of our study is to compare modelled and measured soil carbon balances based on a large scale inventory. The comparison is conducted for the whole of Germany and stratified into forest types or soil units. Furthermore, this study provides data for Forest Management Reference Level for the German Greenhouse Gas inventory. We hypothesized that i) forest types dominated by coniferous trees and/or stocking on base poor soils show higher soil carbon stock changes compared to forest types dominated by broadleaved trees and stocking on base rich sites, ii) Yasso15-results differ most from NFSI-results on sites where NFSI indicates high soil carbon stock changes.
Section snippets
The National Forest Soil Inventory (NFSI)
In Germany the National Forest Soil Inventory (NFSI) is a nationwide survey on a systematic 8 km × 8 km grid on approximately 1900 plots carried out from 1987 to 1994 (NFSI I) and repeated between 2004 and 2008 (Fig. 1). With the NFSI II 1341 plots were resampled (=paired sample) whereas app. 600 were replaced by new established plots (=unpaired sample). A third inventory is planned for 2022. Within the NFSI, all parameters were investigated on a 30 m diameter circular plot with a soil profile
Results
Our simulation indicates that forest soils in Germany accumulate carbon. Overall, the simulated carbon stocks increased from 113 Mg C ha−1 in the year 1990 to 119 Mg C ha−1 in 2030 (Fig. 2a). The simulated carbon balance between 1990 and 2030 exhibits a relatively large variance (Fig. 2b). Most striking is a peak in 1990, which results from a high litter input. The average litter input amounts to 5.30 ± 0.05 Mg C ha−1 a−1 in that year (Fig. 2c). This was due to a series of heavy storms and
Discussion
The simulation as well as the repeated soil inventory indicate that under current conditions, Central European forest soils accumulate considerable amounts of carbon. The results of our modelling approach indicate a high accumulation rate equal to 0.25 ± 0.10 Mg C ha−1 a−1. This rate is somewhat lower, although not statistically different, from the 0.39 ± 0.11 Mg C ha−1 a−1 found in the German NFSI and reported in Grüneberg et al. (2014). Carbon accumulation in forest soils was also found in
Conclusions
The results of this study indicate that German forest soils act on average as a carbon sink. Application of the Yasso15 model confirms the results from the NFSI. On average, the simulated carbon balance is lower than the results of the inventory but the differences are not significant. Accumulated carbon is expected to be sensitive to decomposition, because the most stable SOM-fraction in Yasso15 does not change over time. Adjusting initial values to measured soil carbon stocks yields a close
Acknowledgments
We thank the individual Federal states for providing the datasets as well as the Federal Ministry of Food and Agriculture (BMEL) for facilitating the data analysis. We acknowledge in particular all site investigators and the persons in charge of the individual Federal research stations (http://www.blumwald.de/bze/ansprechpartner-bze) for their assistance and support. We are grateful to the providers of leaf litter data of the UNECE-CLRTAP International Cooperative Programme ICP Forests, to
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