Nitrous oxide emission budgets and land-use driven hotspots for organic soils in Europe

Biogeosciences, 11, 6595–6612, 2014 www.biogeosciences.net/11/6595/2014/ doi:10.5194/bg-11-6595-2014 © Author(s) 2014. CC Attribution 3.0 License. Nitrous oxide emission budgets and land-use-driven hotspots for organic soils in Europe T. Leppelt1, R. Dechow1, S. Gebbert1, A. Freibauer1, A. Lohila2, J. Augustin3, M. Drösler4, S. Fiedler5, S. Glatzel6, H. Höper7, J. Järveoja8, P. E. Lærke9, M. Maljanen10, Ü. Mander8, P. Mäkiranta11, K. Minkkinen12, P. Ojanen12, K. Regina13, and M. Strömgren14 1Thünen Institute of Climate-Smart Agriculture, Braunschweig, Germany 2Finnish Meteorological Institute, Helsinki, Finland 3Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany 4Weihenstephan-Triesdorf University of Applied Sciences, Freising, Germany 5Johannes Gutenberg University, Mainz, Germany 6University of Vienna, Vienna, Austria 7LBEG State Authority for Mining, Energy and Geology, Hannover, Germany 8University of Tartu, Tartu, Estonia 9Aarhus University, Department of Agroecology, Tjele, Denmark 10University of Eastern Finland, Kuopio, Finland 11Finnish Forest Research Institute, Vantaa, Finland 12University of Helsinki, Department of Forest Sciences, Helsinki, Finland 13MTT Agrifood Research Finland, Jokioinen, Finland 14Swedish University of Agricultural Sciences, Uppsala, Sweden Correspondence to: T. Leppelt ([email protected]und.de) Received: 7 May 2014 – Published in Biogeosciences Discuss.: 16 June 2014 Revised: 15 October 2014 – Accepted: 19 October 2014 – Published: 1 December 2014 Abstract. Organic soils are a main source of direct emissions of nitrous oxide (N2O), an important greenhouse gas (GHG). Observed N2O emissions from organic soils are highly variable in space and time, which causes high uncertainties in national emission inventories. Those uncertainties could be reduced when relating the upscaling process to a priori-identified key drivers by using available N2O observations from plot scale in empirical approaches. We used the empirical fuzzy modelling approach MODE to identify main drivers for N2O and utilize them to predict the spatial emission pattern of European organic soils. We conducted a meta-study with a total amount of 659 annual N2O measurements, which was used to derive separate models for different land use types. We applied our models to available, spatially explicit input driver maps to upscale N2O emissions at European level and compared the inventory with recently published IPCC emission factors. The final statistical models explained up to 60% of the N2O variance. Our study results showed that cropland and grasslands emitted the highest N2O fluxes 0.98±1.08 and 0.58±1.03gN2O-Nm−2a−1, respectively. High fluxes from cropland sites were mainly controlled by low soil pH value and deep-drained groundwater tables. Grassland hotspot emissions were strongly related to high amount of N-fertilizer inputs and warmer winter temperatures. In contrast, N2O fluxes from natural peatlands were predominantly low (0.07±0.27gN2O-Nm−2a−1) and we found no relationship with the tested drivers. The total inventory for direct N2O emissions from organic soils in Europe amount up to 149.5GgN2O-Na−1, which also included fluxes from forest and peat extraction sites and exceeds the inventory calculated by IPCC emission factors of 87.4GgN2O-Na−1. N2O emissions from organic soils represent up to 13% of total European N2O emissions reported in the European Union (EU) greenhouse gas inventory of 2011 from only 7% of the EU area. Thereby the model demonstrated that the major part (85%) of the inventory Published by Copernicus Publications on behalf of the European Geosciences Union. 6596 T. Leppelt et al.: N2O hotspots from EU organic soils is induced by anthropogenic management, which shows the significant reduction potential by rewetting and extensification of agriculturally used peat soils. 1 Introduction Nitrous oxide (N2O) is a natural trace gas with increasing abundance in the atmosphere and radiation-enforcing properties. Soil processes are the dominant source of terrestrial N2O and contribute about 70% to the total net emission budget of N2O (Mosier, 1998). Maljanen et al. (2010) showed that N2O emissions from organic soils in Nordic countries are 4 times higher in comparison to fluxes from mineral soils. In Europe about 7% of the land area is covered by organic soils, often also called peat soils, according to Montanarella et al. (2006). The N2O fluxes from natural, waterlogged organic soils are low. Drainage and cultivation lead to N mineralization from degrading peat, and consequently N2O production (Wild et al., 1998; Regina et al., 2004) via nitrification and denitrification processes (Firestone and Davidson, 1989). Thus far, large-scale estimates have been based on static emission factor approaches, which only partly reflect land use, climate, soil nutrient or drainage status. A regional study from Estonia found significant land use differences in N2O emissions from drained organic soils (Mander et al., 2010). The IPCC recently published new emission factors for different land use types, climate regions and basic soil nutrient and drainage categories for global application in the IPCC supplement for national greenhouse gas (GHG) inventories on wetlands (IPCC, 2013). Application of emission factors in GHG inventories can lead to high uncertainties (Pouliot et al., 2012). At present, there are no process-based models of N2O fluxes for organic soils that could be upscaled or explain the variability of measured N2O fluxes from European peatlands better than average emission factors. A successful upscaling of an empirical model could reduce the uncertainty of emission budgets by including functional relationships to driving parameters. Klemedtsson et al. (2005) suggested to model N2O emissions from peatland forest in Sweden with an empirical relationship to C/N ratio of topsoil, based on observations from 12 sites. In Great Britain, N2O emissions from agricultural organic soils were modelled with a regression to N input, water-filled pore space (WFPS), soil temperature and land use (Sozanska et al., 2002), based on observations from 59 sites predominantly from mineral soils. The long reference lists in the 2013 IPCC supplement suggest that there are a large amount of N2O observations in the literature that have not yet been used for model calibration and validation. While some regionand land-use-specific empirical relationships have been published (Klemedtsson et al., 2005; Mander et al., 2010), a generic functional relationship between N2O and environmental and management drivers across land use categories is missing. This hampers the development of management strategies at local, national and European scale for organic soils that reduce anthropogenic N2O emissions. This study aims to 1. develop generic empirical relationships between human and natural drivers of N2O applicable across land use types by means of multi-site calibration with all observations published until mid-2013 in Europe; 2. determine the N2O budget of organic soils in Europe and its various sources of uncertainty (model, spatial driver data); 3. determine spatial hotspots of N2O emissions driven by land use, other human or natural drivers and priorities for future observations in high-N2O-risk zones; 4. test whether the new IPCC emission factors are spatially representative of Europe and quantify potential bias. 2 Material and methods 2.1 Database The N2O flux synthesis is based on a meta-study of direct N2O emissions from organic soils. This literature survey contains N2O observations in Europe published until mid-2013. All incorporated in situ flux measurement studies used the same gas measurement method – the well-established closedchamber technique (Hutchinson and Mosier, 1981). Annual N2O fluxes were directly taken out of the publications and all fluxes that fulfil the minimum criteria of 12 measurements per year were included in our analysis. The database contains the total amount of 659 annual flux measurements made on 109 sites in temperate and boreal regions in Europe, spread across the main organic soil regions (Fig. 1). Numerous measurements came from central Europe (Germany, Netherlands) and from northern European countries like Finland, Sweden and Estonia, whereas the British Isles and eastern and southern Europe are under-represented in the dataset. The number of measurements per site differs from a minimum of 1 annual flux period up to a total amount of 59 annual fluxes. Most of the sites include flux measurements from different plots that vary in management and environmental conditions. In part, the experimental design was purposely chosen to distinguish between treatments or influences from different sources, e.g. nitrogen fertilizer (Velthof and Oenema, 1995) or water content of topsoil (van Beek et al., 2010). We extracted diverse environmental and management parameters to derive a wide set of parameters that can be tested for potential relationships to N2O fluxes. The most frequent parameters are listed in Table 1 with units, parameter ranges and fraction of coverage in the studies. Missing values for climate parameters were gap-filled with data from the European Climate Assessment and Dataset (ECAD), described Biogeosciences, 11, 6595–6612, 2014 www.biogeosciences.net/11/6595/2014/ T. Leppelt et al.: N2O hotspots from EU organic soils 6597 Table 1. List of potential driving parameters for N2O with units, value mean/range and fraction of measurement studies that cover each parameter. Soil parameters are related to topsoil layer of 100cm depth and all parameters are calculated as annual average values, with the exception of precipitation and nitrogen fertilization, which are calculated as annual sums. Name Description Unit Mean Min Max Fraction (%) bd Bulk density g×cm−30.34 0.03 1.36 69.2 corg Organic carbon content % 36.11 6.7 57.5 79.8 ntot Total nitrogen content % 1.82 0.3 3.9 71.8 ph pH value – 5.34 3.3 7.63 61 cn Ratio of carbon and nitrogen – 21.29 9 78.17 80.4 pd Thickness of peat layer m 1.61 0.2 10.2 38.7 tair Air temperature ◦C 6.22 –0.23 11.2 83.5 tsoil Soil temperature ◦C 8.8 1.94 11.78 19.1 pp Precipitation mm 645.2 0 1840 81.6 wt Groundwater table m 0.32 –0.62 1.36 82.2 wfps Water-filled pore space % 76.48 41.25 100 13.7 no3 Nitrate concentration kg×ha−132.97 0 211.7 13.1 nh4 Ammonia concentration kg×ha−128.4 0.33 241 13.1 nmin Mineral nitrogen concentration kg×ha−161.37 2.21 241 14.3 nfert Organic and mineral nitrogen fertilization kg×ha−143.77 0 713 80.7 in Haylock et al. (2008). All of the database references are listed in Table 6. 2.2 Model development, calibration and validation Firstly, the N2O fluxes and potential drivers were analysed by means of univariate statistics. Furthermore we investigated the correlations between fluxes and the corresponding driving parameters to understand interactions and constrain parameter combinations. The specified statistical analyses were carried out with the programming language R (R Development Core Team, 2013). Based on these results we used an empirical fuzzy logic modelling approach to predict N2O fluxes based on main driving parameters. This datadriven fuzzy logic model has been applied to predict and upscale annual N2O fluxes for agricultural mineral soils in Germany. The model performance was superior to other empirical approaches and explained up to 72% of the variability in the dataset. (Dechow and Freibauer, 2011). Bardossy et al. (2003) describe the fuzzy-based modelling as a fast, transparent and parameter-parsimonious alternative to other approaches. These techniques are based on the concept of fuzzy logic, a set theory that extends the binary logic of true (1) and false (0). It allows for fuzzy sets with truth values in the range between 0 and 1 (degree of fulfilment) to be had, and is therefore able to handle partial truth, uncertainties or so-called fuzziness. The fuzzy sets can be used to classify factor domains not only by constant crisp sets but also by different function types (e.g. triangular, quadratic) with variable membership grade over the factor domain. Furthermore it can be utilized to divide factor spaces into sub-domains and calculate all possible combinations in fuzzy interference schemes (FISs) using fuzzy logic algebra. These FISs can be merged in conditional rule systems to model multivariate problems. The approach is able to model non-linear relationships and to represent a priori knowledge that limits parameter spaces or constrains directions of relationships. Another advantage of fuzzy sets in comparison to other decision tree approaches is the smooth transition between different sets that allows for more accurate modelling of continuous variables. In this study triangular fuzzy sets for driving parameters of annual N2O fluxes were calibrated using a simulated annealing technique to optimize corresponding responses for N2O flux measurements. We use a forward selection algorithm in combination with a sub-dataset, which consists of drivers that are available at European level, to determine the best-fitted and regionalizable parameter combinations. The Nash–Sutcliffe efficiency (NSE) was used for model assessment: NSE =1− n P i=1Fi o−Fi m2 n P i=1Fi o−¯ Fo2.(1) The coefficient ranges from −∞ to 1, where the value of 1 corresponds to a perfect match and a value of 0 indicates an accuracy comparable to the mean of the observed data. The residual variance of the observed fluxes Fi oand the modelled fluxes Fi mmust be smaller than the data variance of the observed fluxes to indicate that the model is a better predictor than the mean value of the observed data Fo. The NSE coefficient is described as a good indicator of model prediction performance because it is a combined measure for scatter and bias (Nash and Sutcliffe, 1970). The www.biogeosciences.net/11/6595/2014/ Biogeosciences, 11, 6595–6612, 2014 6598 T. Leppelt et al.: N2O hotspots from EU organic soils Figure 1. Overview for measurement sites. Size of points indicates number of measurements per site. Background map displays peatland distribution in Europe with peat cover per square kilometre from Montanarella et al. (2006). automaticallyselectedparameter combinations with the highest NSE measures above 0 represent the best N2O predictors according to the parameter set and performance indicator used. The NSEcali and NSEcv refer to the NSE coefficient for the model calibration and the validation, respectively. Further optimization was performed by setting up model ensembles (MODE) for final parameter combinations, using empirical bootstrapping methods with up to 50 individual models, which reduces over-fitting and achieves better averaged model predictions. We followed the procedures described in Dechow and Freibauer (2011). We validated the model results by a k-fold cross validation by study sites (Kohavi, 1995). The original dataset was partitioned into ksubsets by study site. A single subsample was excluded as a validation dataset from the calibration process. All remaining k-1 sites were used for model calibration and could be validated to the independent validation set. This procedure is repeated ktimes until each site is used once as a validation dataset. The study sites subsamples include a different number of annual fluxes, which can contain up to 15% of the fluxes from the total dataset. Hence the unequally sized subsamples can lead to a very strict cross-validation result in the case of excluding a site with numerous measurements and high proportion of the total dataset. The calibration was weighted by number of measurements per site to avoid overand under-representation for sites with small and high numbers of flux measurements, respectively. We also have to take into account that the N2O fluxes span over several orders of magnitude. Hence we applied a logarithmic transformation, Fl o=ln(F i o+0.5), (2) to linearize the flux range for better optimization performance. To generate models useful for upscaling, we considered only driving parameters that can be regionalized. Therefore good predictors of N2O fluxes like soil nitrate (NO− 3), ammonium (NH+ 4), mineral N content or CN ratio were not included in the final modelling approach. 2.3 Regionalization The regionalization describes the application of our validated fuzzy model on EU-wide available input datasets to derive consistent N2O emissions for Europe. Spatially explicit upscaling of the fuzzy model was realized in a geographic information system (GIS). We used the open source GRASS GIS (Neteler et al., 2012) to process the model input datasets and predict N2O emissions at the EU level. Therefore we developed and implemented several GRASS modules to perform fuzzy logic modelling in this GIS framework. Additionally we conduct time series analysis of climate and land use data by using the temporal framework TGRASS (Gebbert and Pebesma, 2014). The input data at the EU level is predominantly available in raster cell format in Lambert azimuthal equal area (LAEA) projection, with the finest resolution of 1km×1km gridded data. Hence we selected the LAEA projection and a resolution of 1km×1km as a common unit to avoid data loss from transformation processes and raster cell resampling. The model was applied for peatland areas in Europe which are based on the organic soil distribution map by Montanarella et al. (2006). This dataset serves as a basis for all spatial calculations in this study. The following regional datasets were used for driving parameters: –Land use distribution: –CORINE land cover (CLC) from 2006 (Büttner and Kosztra, 2007) differentiated into cropland, grassland, forest, peat extraction and natural areas. –Historic Land Dynamics Assessment (HILDA) (Fuchs et al., 2013) differentiated into cropland, grassland (which also contains natural areas) and forest sites for the latest available year: 2010. –Meteorology: temperature and precipitation from the ECAD dataset (Haylock et al., 2008). Based on the daily resolution dataset, we calculated the 30-year (1982–2012) long-term annual and seasonal (spring, summer, autumn and winter) minimal, maximal and mean temperatures and precipitations sums. –Mean annual water table: there are no spatially explicit data available for Europe. Mean annual water table was therefore represented by land-use-specific frequency distribution functions of observed water table in Biogeosciences, 11, 6595–6612, 2014 www.biogeosciences.net/11/6595/2014/ T. Leppelt et al.: N2O hotspots from EU organic soils 6599 the database. The mean value of the frequency distributions was used for regionalization, while the distribution served for uncertainty assessment. –Soil properties: datasets from the European soil portal and the Joint Research Centre (JRC) (Panagos et al., 2012). –Topsoil acidity (Reuter et al., 2008). –Organic carbon content of topsoil (Jones et al., 2005). –Bulk density of topsoil (Tiktak et al., 2002). The European soil portal provides gridded averages, which mix mineral and organic soils. Consequently, bulk density neither adequately reflects organic soils nor the dependence of bulk density on land use and peat degradation status. As for mean annual water table, land-use-specific frequency distribution functions of bulk density were used for regionalization. –Nitrogen fertilization based on Hutchings et al. (2012). The sum of Europe-wide annual N2O emissions represents the emissions from cropland, grassland, forest, peat extraction and natural sites on organic soils. Besides the fuzzy model approach, land-use-stratified emission factors can also be utilized to predict annual emission budgets. Emission factors were derived from the N2O flux synthesis as mean per land use type and compared to the IPCC emission factors from the wetland supplement (IPCC, 2013). We used the good practice guidance of the IPCC tier 1 approach to calculate the European inventory of N2O emissions from managed organic soils. The IPCC tier 1 approach stratifies land use classes by drainage, peat type and climate zone. The delineation between the temperate and boreal zone can be derived from the IPCC definition applied to climate data. Drainage and peat type, however, are not available in a spatially explicit way. We therefore applied the default of nutrient-poor conditions in boreal forests, nutrient-rich conditions in temperate forests, and deep drainage in temperate grasslands. Spatial resolution and land use definitions produce significant uncertainty in the regionalization of N2O emissions. The uncertainty in land use classifications was assessed by testing the sensitivity of the European N2O inventory to the choice of the land use map, represented by the two Europe-wide spatially explicit map products CORINE and HILDA. The general land use distribution on organic soils can be separated into the forestry-dominated boreal zone, the agricultural temperate zone and the main natural peatland areas in the subarctic northern parts of Europe. N2O emission hotspots were identified on the map together with related ranges of drivers separately for each land-use-specific model. In order to locate N2O emission hotspots in Europe, we computed the flux distributions by land use category from the N2O emission map and defined the fluxes above the 90th quantile as hotspot emissions for the particular land use category. 2.4 Uncertainty analysis N2O emissions can vary largely in space and time and the capabilities to model these variation are restricted to the size of the sample dataset and the data quality. Therefore it is important to propagate the uncertainties during the modelling process in order to be able to estimate the overall accuracy of the model result. For several ecosystems the confidence interval limits of IPCC emission factors for N2O emissions from peat soils are greater than the mean values. The modelling approach aims to reduce this variability by using explanatory parameters to predict N2O fluxes. Uncertainty analysis comprised uncertainties in input parameters and in the model. The model uncertainty was calculated by means of a fuzzy rule-based uncertainty estimation (details in Dechow and Freibauer (2011)). It can be described as the standard deviation σf, which is derived from the rule-specific normallike uncertainty distributions in Eq. (3): σ2 f= n P i=1DOFiσ2 ri n P i=1DOFi (3) where DOFiis the degree of fulfilment and σriis the standard deviation of a normal-like uncertainty distribution of rule i. The rule specific uncertainty was estimated by using results from the cross validation over study sites as a reference to calculate the model uncertainty. The input parameter uncertainties were estimated by Monte Carlo simulation with parameter variabilities taken from available databases. The combination of input and model uncertainty results in the overall uncertainty estimation, which was applied pixel-wise for uncertainty analysis at the EU level. The resulting map contains average and standard deviation values for a normallike distribution function of N2O emissions for each raster cell. The N2O emission budget is the sum of all raster cell values that are located within a defined area. The corresponding uncertainty of the inventory can be calculated by error propagation. Spatially explicit modelling introduces autocorrelation into the calculation of GHG emission inventories and their uncertainty estimation. Without consideration of the spatial covariance we would underestimate the real uncertainty. This is a methodical problem that we solved by considering the covariance in the error propagation equation to improve the uncertainty estimation: σb=v u u t n X i=1 σ2 fi+2 n−1 X i=1 n X j=i+1 covij ,(4) where σi,j is the standard deviation of a raster cell, indexed by i, and covij is the corresponding covariance between all www.biogeosciences.net/11/6595/2014/ Biogeosciences, 11, 6595–6612, 2014 6600 T. Leppelt et al.: N2O hotspots from EU organic soils Figure 2. Box plots for N2O fluxes (a) and mean annual groundwater table (b) for five different land use categories (cropland, grassland, peat extraction, forest and natural sites). N2O fluxes are shown without outliers and nindicates the number of measurement per category. raster cell values, indexed by iand j. We approximated the covariances between raster cells as a function of distance and calculated the corresponding covariance matrix to apply Eq. (4) to the raster map. 3 Results and discussion 3.1 Statistical analysis The N2O fluxes were log-normal-distributed with predominantly minor fluxes between −0.1 and 0.1 and few high peaks up to 8.11gN2O-Nm−2a−1from grasslands in the Netherlands (Koops et al., 1997). We found significant differences in flux data between land use categories; these are shown in Fig. 2. In general the highest fluxes occurred on cropland and grassland sites, whereas natural and rewetted organic soils feature low emissions on average. Fluxes from forest sites were on average lower than the emissions from cropland and grasslands, but included some high outliers of up to 6.06gN2O-Nm−2a−1from Slovenia (Danevˇ ciˇ c et al., 2010). The peat extractions sites were only represented by 35 annual flux measurements, which indicated an average flux of 0.47gN2O-Nm−2a−1for active and abandoned extraction sites. Table 2 lists the correlation coefficients for N2O fluxes and main driving parameters. The mean annual groundwater tables for different land use categories were correlated to N2O fluxes with a correlation coefficient of r=0.32 (p < 0.05). In addition, Fig. 3a shows that high N2O fluxes occurred in the range of mean groundwater table of 0.2 to 0.9m below the surface. The groundwater table has been found to be a driving parameter for N2O in sevFigure 3. The scatter plots show (a) the N2O flux relationship to mean annual groundwater table, (b) the relationship between N fertilization and N2O fluxes for cropand grassland with significant (P < 0.001) linear relationship for grassland (r2=0.26), (c) the N2O fluxes plotted against the C/N ratios, and (d) pH values in relation to these C/N ratios including the fitted non-linear function (ph=15cn−0.36) (r2=0.5). eral other studies (Martikainen et al., 1993; Regina et al., 1996; van Beek et al., 2010). Drainage increases emissions of N2O, in particular for nutrient-rich organic soils and fertilized and grazed grassland. The seasonal fluctuations of the water table could explain more variability of N2O emissions, but this information was only available for a small fraction of the dataset. Therefore we were restricted to the use of only the mean annual water table in our analysis. The N-fertilization amount was also correlated with N2O fluxes (r=0.43, p < 0.05). Figure 3b suggests that this relationship is especially strong for emissions from grasslands. The N2O fluxes plotted against the C/N ratio indicated a ratio threshold at approximately 30–35 below which high fluxes occur in the dataset (see Fig. 3c). This result provides evidence and supports the findings of Klemedtsson et al. (2005) that the C/N ratio can be a strong predictor of N2O emissions from organic soils. Peat mineralization releases carbon as CO2, while nitrogen preferentially remains in the soil. Nitrogen fertilization has a similar net effect, and thus both processes reduce the soil C/N ratio. Therefore the C/N ratio can be utilized as an indicator of soil processes and conditions that trigger N2O emissions. Figure 3d shows that low pH values were related to high C/N ratios and vice versa. The collected site data revealed a non-linear relationship between pH values and corresponding soil C/N ratios. Due to unavailable data for C/N ratios at the European level, the soil Biogeosciences, 11, 6595–6612, 2014 www.biogeosciences.net/11/6595/2014/ T. Leppelt et al.: N2O hotspots from EU organic soils 6601 Table 2. Correlation matrix of N2O fluxes and potential driving parameters for the available dataset from organic soils in Europe. The parameter names are described in Table 1. N2O bd corg ntot ph cn pd tair tsoil pp wt wfps nmin nfert N2O 1.00 0.17 −0.10∗0.07 −0.05 −0.19 −0.14∗0.06 0.07 0.11∗∗ 0.32 −0.30 0.10 0.43 bd 1.00 −0.80 −0.39 0.37 −0.48 −0.32 0.34 −0.08 −0.17 0.46 0.07 −0.04 0.25 corg 1.00 0.38 −0.50 0.59 0.27 −0.32 −0.08 0.15 −0.31 −0.12 −0.12 −0.13∗∗ ntot 1.00 0.14∗−0.40 0.34 0.04 0.11 −0.04 −0.21 0.07 0.26∗0.16 ph 1.00 −0.64 −0.31 0.06 0.22∗−0.30 0.29 −0.03 0.29∗∗ 0.19 cn 1.00 0.02 −0.22 −0.18 0.15 −0.20 −0.19 −0.36 −0.18 pd 1.00 0.17∗∗ 0.29∗0.10 −0.39 −0.06 −0.20 −0.22 tair 1.00 0.77 0.02 −0.11∗−0.01 0.15 0.16 tsoil 1.00 0.44 0.15 −0.26 0.27 0.07 pp 1.00 −0.13∗∗ −0.14 0.24∗0.01 wt 1.00 −0.39 0.08 0.17 wfps 1.00 0.10 −0.01 nmin 1.00 0.10 nfert 1.00 Level of significance: ∗∗ significant at P≤0.01,∗significant at P≤0.05. pH relationship to C/N ratios was used as partial proxy for C/N ratio in the regionalization. There is a general trend that managed organic soils with low C/N ratio occur on fertile, minerotrophic peat soils with higher pH values while high C/N ratios are found in nutrient-poor ombrotrophic peatlands. Nevertheless, the wide scatter of pH values for a given C/N ratio indicates more complex spatial patterns, and pH also has an independent direct influence on N2O formation (see below). Several other studies found evidence for climate influence at particular peatland sites or regions(Dobbie et al., 1999; Sozanska et al., 2002; Lohila et al., 2010), which can be confirmed in the following land-use-stratified models. 3.2 Model calibration and validation 3.2.1 Complete dataset We applied the fuzzy logic model approach for the entire flux dataset, which results in the best-fitted model ensemble (NSEcv =0.12) for four covariates (bulk density, groundwater table, mean winter temperate and annual precipitation). The stochastic variability within the data prevents the generic model approach from predicting the measured fluxes accurately. Thus validation results were unsatisfactory and we investigated further improvements using data partitioning with categorical parameters such as land use category, peat type and climate zone. The peat-type-stratified dataset, separated into bog, fen and shallow peat soils, results in improved model fits for each peat type. Peat type, however, cannot be regionalized due to the lack of European spatially explicit maps. In contrast to Freibauer and Kaltschmitt (2003), where N2O fluxes from temperate and sub-boreal climates on mineral soils showed different mean and maximum emissions, we found no significant differences between climate zones for N2O fluxes on organic soils. Hence the data partitioning by climate zones had no improving effect on the model performance. We achieved the best model results for land use stratification and separately developed fuzzy logic models for cropland, grassland, forest and extraction sites. Therefore each land use model has a different number and range of observations, as well as different covariates. Table 3 gives an overview for the land-use-specific N2O flux data and corresponding model performances. 3.2.2 Cropland The best-fitted cropland model has a model efficiency of NSE=0.63 and was calibrated for three parameters – topsoil pH, the mean groundwater table and the annual precipitation amount. These model covariates were validated for 40 observations from 20 sites on which all three model parameter were available in our dataset. The range of N2O fluxes from the cropland sub-dataset (−0.02, 3.70) in gN2O-Nm−2a−1 was comparable to the range of the complete cropland dataset (−0.02, 6.10). Only a few extremely high fluxes were excluded, and so the mean values are equivalent. Using this sub-dataset, we were able to achieve the best model fit of NSEcv =0.41 in terms of an independent cross validation (compare Fig. 4). As has been mentioned in Sect. 3.1, the topsoil pH of croplands was not only correlated to N2O emissions (r=−0.53, p < 0.001) but also significantly to the C/ N ratio (r=−0.68, p < 0.001). Mørkved et al. (2007) suggested the soil pH to be a strong controlling factor for N2O fluxes because it affects the N2O production processes of both denitrification and nitrification. Additionally they stated that low-pH soils have higher N2O/N2production ratios and thus higher potential N2O emissions. The described effect is also observable for fluxes from croplands on organic soils. Weslien et al. (2009) also found a strong negative correlation of soil pH and N2O emissions in their data. They argued that the dinitrogen www.biogeosciences.net/11/6595/2014/ Biogeosciences, 11, 6595–6612, 2014 6602 T. Leppelt et al.: N2O hotspots from EU organic soils Table 3. List of calibrated and validated N2O fuzzy logic models with covariates that are described in Table 1 (Parameters), number of flux measurements (Nflux) and model performances of calibration (NSEcali) and cross validation (NSEcv) for different land use categories, respectively. Land use Parameters Nflux NSEcali NSEcv Crop wt, ph, pp 40 0.63 0.41 Grass nfert, tair winter, 96 0.68 0.58 pp autumn Forest wt, ph, tair 60 0.66 0.25 Extraction bd, pp, tair winter 21 0.89 0.28 Natural – 132 – – oxide reductase is inhibited by acidic pH and can thus enhance N2O emissions (Firestone and Davidson, 1989; Skiba and Smith, 1993). This result is supported by the findings of Liu et al. (2010), who found a strong negative correlation between the N2O/N2product ratio of denitrification and soil pH. The second important parameter in the model, the groundwater table, is well known as a proxy for oxygen availability in topsoil and can therefore significantly control the N2O production processes (Regina et al., 1996; van Beek et al., 2010). We found a correlation between N2O and groundwater table in the cropland dataset which confirmed this significance (r=0.31, p < 0.05). The model indicates that deep drainage induces higher fluxes of N2O. In contrast to Fig. 3a, which includes all land use categories, the model structure for the relationship of groundwater table and N2O fluxes for croplands only was linear and not in the form of a humpshaped, non-linear curve. The sub-dataset for croplands indicated a linear increase in N2O fluxes with deep drainage. Furthermore, precipitation emerged as the third model component. Precipitation increases the WFPS in topsoil and can trigger N2O flux peaks immediately after the rain events (Dobbie et al., 1999; Dobbie and Smith, 2003). High annual precipitation amounts can increase the probability of such N2O peak flux events in drained agriculturally used organic soils. The expected role of N fertilizer, i.e. as a N2O emission amplifier on croplands (Velthof and Oenema, 1995; Skiba et al., 1998), could not be confirmed in our modelling approach. Both the statistical analysis, shown in Fig. 3b, and the fuzzy modelling approach found no significant relationship of N2O fluxes and N fertilization. Organic soils under croplands had C/N ratios below 30 and are likely strong sources of nitrogen from peat mineralization. Assuming a soil carbon loss from mineralized peat of 7.9 tCha−1a−1, as suggested by the IPCC (IPCC, 2013, Table 2.1), it would result in a mean N mineralization of approximately 424.7 kgha−1a−1 for cropland sites in our database with average C/N ratios of 18.6±5.8. This exceeds the maximum amount of N fertilizer (288.8kgha−1) that has been applied to cropland sites. The Figure 4. Fuzzy model performance for calibration and cross validation of N2O fluxes from cropland on organic soils. The modelled fluxes (xaxis) represent the mean flux rates from a model ensemble of 50 individually bootstrapped models. The cross validation was performed by excluding one site per iteration. Figure 5. Fuzzy model results for calibration and cross validation for N2O fluxes from grassland on organic soils. The modelled fluxes (xaxis) represent the mean flux rates from a model ensemble of 50 individually bootstrapped models. The cross validation was performed by excluding one site per iteration. estimated mean N mineralization suggests that, independent of fertilizer application, sufficient substrate for N2O production is available and that the N2O production is not limited by external N input. All high fluxes from croplands were measured on deeply drained sites, which is also reflected in the regionalization by using the groundwater distribution with a mean water table of 0.58m below surface. In summary, sensitivity analysis shows that the cropland model predicts the highest emissions on sites with a combination of deep drainage, a soil pH around 4.0 and a high amount of annual precipitation, whereas the lowest emissions occur for soils with higher pH values and water table near the surface, regardless of rainfall. 3.2.3 Grassland Grasslands are the best-observed land use category, represented by 217 annual flux measurements. The automatic calibration results in a fuzzy model with three parameters, which can explain about 68% of the variability in the flux data (NSE=0.68). The parameters are nitrogen fertilizer amount, Biogeosciences, 11, 6595–6612, 2014 www.biogeosciences.net/11/6595/2014/ T. Leppelt et al.: N2O hotspots from EU organic soils 6603 Figure 6. Fuzzy model results for calibration and cross validation for N2O fluxes from forest sites on organic soils. The modelled fluxes (xaxis) represent the mean flux rates from a model ensemble of 50 individually bootstrapped models. The cross validation was performed by excluding one site per iteration. mean winter temperature and precipitation in autumn. The required parameter combination is available for 96 observations from 44 sites that cover the N2O flux range of (−0.03, 4.10) with a higher mean (¯x=0.67) than the complete grassland dataset (¯x=0.58gN2O-Nm−2a−1). The cross validation could reproduce nearly 60% of the variability in the data (NSEcv =0.58) (Fig. 5). In agreement with the statistical analysis (Fig. 3b), we also found the significant relationship of N2O fluxes and N fertilization for the grasslands fuzzy model approach. The amount of N fertilization was directly correlated (r=0.54, p < 0.05) to the fluxes from grassland sites, whereas no relationship was found for croplands. In fact, the N-fertilization amount was the most important model parameter. The importance of N fertilization has been recognized in several other studies on organic soils (Velthof and Oenema, 1995; Skiba et al., 1998). The different responses for grassland and cropland have also been observed and modelled for N2O fluxes from mineral soils (Dechow and Freibauer, 2011). Furthermore different sensitivities to N fertilization on temperate and sub-boreal agricultural mineral soils are discussed in Freibauer and Kaltschmitt (2003) and Roelandt et al. (2005). In addition to the management influence, the mean winter air temperature is also correlated to N2O fluxes (r=0.40, p < 0.05)and wasidentified asasecond importantmodelparameter. The emissions increased with rising winter air temperatures up to maximum values approximately around 0◦C. This relation of N2O fluxes to mean temperatures in winter months (December, January and February) can be a proxy for the amount of released emissions due to freeze–thaw cycles as described in Freibauer and Kaltschmitt (2003) and Jungkunst et al. (2006). Although the interaction of parameters, e.g. air temperature, WFPS and snow cover, that can induce freeze–thaw cycles is complex and highly variable, the model successfully worked with winter temperature as a simple input parameter. This is especially useful with regard to model upscaling attempts, because the temperature, Figure 7. Fuzzy model results for calibration and cross validation for N2O fluxes from peat extraction sites on organic soils. The modelled fluxes (xaxis) represent the mean flux rates from a model ensemble of 50 individually bootstrapped models. The cross validation was performed by excluding one site per iteration. as well as the winter temperature only, is easily available at the European level. Autumn precipitation emerged as the third model component. We observed a positive correlation (r=0.50, p < 0.05) between the rainfall amount in autumn months (September, October and November) and the N2O fluxes on grassland sites. As stated before, precipitation can increase the WFPS in topsoil and trigger N2O fluxes (Dobbie et al., 1999). This strong statistical relation between autumn precipitation and N2O has not been described before for organic grasslands, but agrees with evidence in mineral croplands Dechow and Freibauer (2011). High precipitation in autumn leaves wet soils in winter, which is a precondition for freeze–thaw peaks of N2O emissions. In summary, grasslands N2O fluxes are sensitive to N fertilization and seasonal precipitation and temperatures. Highest emissions are expected for intensively managed grasslands with high N input, which are controlled by winter temperature and rainfall events in autumn. 3.2.4 Forest The measured forest N2O fluxes in the dataset (n=170) are dominantly located in boreal (61%) and sub-boreal regions (22%), whereas temperate forest sites make up only a small percentage (17%). These climatic regions have different mean N2O emissions 0.51, 0.33 and 0.26 ingN2ONm−2a−1for temperate, sub-boreal and boreal climates, respectively. However the range within the climatic regions are comparable and no significant difference between mean N2O fluxes is recognizable. The best-fitted forest model consisted of three parameters: mean groundwater table, topsoil pH and the annual mean air temperature with a model efficiency of NSE=0.66. 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