Signe K Borgen

Research Scientist

(+47) 974 77 882
signe.borgen@nibio.no

Place
Ås H8

Visiting address
Høgskoleveien 8, 1433 Ås

Abstract

Boreal forests contain 30% of the global forest carbon with the majority residing in soils. While challenging to quantify, soil carbon changes comprise a significant, and potentially increasing, part of the terrestrial carbon cycle. Thus, their estimation is important when designing forest-based climate change mitigation strategies and soil carbon change estimates are required for the reporting of greenhouse gas emissions. Organic matter decomposition varies with climate in complex nonlinear ways, rendering data aggregation nontrivial. Here, we explored the effects of temporal and spatial aggregation of climatic and litter input data on regional estimates of soil organic carbon stocks and changes for upland forests. We used the soil carbon and decomposition model Yasso07 with input from the Norwegian National Forest Inventory (11275 plots, 1960–2012). Estimates were produced at three spatial and three temporal scales. Results showed that a national level average soil carbon stock estimate varied by 10% depending on the applied spatial and temporal scale of aggregation. Higher stocks were found when applying plot-level input compared to country-level input and when long-term climate was used as compared to annual or 5-year mean values. A national level estimate for soil carbon change was similar across spatial scales, but was considerably (60–70%) lower when applying annual or 5-year mean climate compared to long-term mean climate reflecting the recent climatic changes in Norway. This was particularly evident for the forest-dominated districts in the southeastern and central parts of Norway and in the far north. We concluded that the sensitivity of model estimates to spatial aggregation will depend on the region of interest. Further, that using long-term climate averages during periods with strong climatic trends results in large differences in soil carbon estimates. The largest differences in this study were observed in central and northern regions with strongly increasing temperatures.

To document

Abstract

Soil organic carbon (C), accumulated over millennia, comprise more than half of the C stored in boreal and temperate forest landscapes. We used the Norwegian national forest inventory and soil survey network (n = 719, no deep organic soils) to explore the validity of a deterministic model representation of this pool (Yasso07). We statistically compared simulated and measured soil C stocks and related differences (measured – simulated) to site factors (drainage, topography, climate, vegetation, C-to-N ratio, and soil classification). Median C stocks were 5.0 kg C·m−2 (model) and 14.5 kg C·m−2 (measurements). Soil C differences related to site factors (r2 of 0.16 to 0.37). For Brunisols, Gleysols, and wet Organic soils, differences related primarily to topographic wetness. For Regosols, Podzols, and Dystric Eluviated Brunisols, they related to climate, profile depth, and, in some cases, drainage class and site index. We argue that soil moisture regimes in our study area overrule tree productivity effects in the determination of soil C stocks and present conditions for soil formation that the model cannot (and does not explicitly) account for. These are processes such as humification and podsolization that involve eluviation and illuviation of dissolved organic C (DOC) with sesquioxides to form spodic B horizons and carbon enrichment due to hampered decomposition in frequently anoxic conditions.

Abstract

Reliable methods are required to predict changes in soil carbon stocks. Process-based models often require many parameters which are largely unconstrained by observations. This induces uncertainties which are best met by using repeated measurements from the same sites. Here, we compare two carbon models, Yasso07 and Romul, in their ability to reproduce a set of field observations in Norway. The models are different in the level of process representation, structure, initialization requirements and calibration- and parameterization strategy. Field sites represent contrasting tree species, mixture and soil types. The number of repetitions of C measurements varies from 2 to 6 over a period of up to 35 years, and for some of the sites, which are part of long-term monitoring programs, plenty of auxiliary information is available. These reduce the danger of overparametrization and provide a stringent testbed for the two models. Focus is on the model intercomparison, using identical site descriptions to the extent possible, but another important aspect is the upscaling of model results to the regional or national scale, utilizing the Norwegian forest inventory system. We suggest that a proper uncertainty assessment of soil C stocks and changes has to include at least two (and preferably more) parametrized models.

Abstract

Reliable methods are required to predict changes in soil carbon stocks. Process-based models often require many parameters which are largely unconstrained by observations. This induces uncertainties which are best met by using repeated measurements from the same sites. Here, we compare two carbon models, Yasso07 and Romul, in their ability to reproduce a set of field observations in Norway. The models are different in the level of process representation, structure, initialization requirements and calibration- and parameterization strategy. Field sites represent contrasting tree species, mixture and soil types. The number of repetitions of C measurements varies from 2 to 6 over a period of up to 35 years, and for some of the sites, which are part of long-term monitoring programs, plenty of auxiliary information is available. These reduce the danger of overparametrization and provide a stringent testbed for the two models. Focus is on the model intercomparison, using identical site descriptions to the extent possible, but another important aspect is the upscaling of model results to the regional or national scale, utilizing the Norwegian forest inventory system. We suggest that a proper uncertainty assessment of soil C stocks and changes has to include at least two (and preferably more) parametrized models.

Abstract

Every year the Norwegian Forest and Landscape Institute submits the national GHG inventory for the land use, land-use change and forestry sector as part of the National Inventory Report (NIR). The methodology and activity data used to estimate CO2 emissions and removals from cropland and grassland were thoroughly evaluated in 2012 and several new methods were implemented in the 2013 NIR submission. The objective of this report is to present the results of this evaluation and to provide detailed documentation of the new methodologies and the emissions reported in the 2013 NIR submission to UNFCCC for cropland and grassland (CPA, 2013). This report describes four major topics: 1) Method choice for mineral soils. The erosion-based method previously used for mineral soils on both cropland and grassland cannot be considered appropriate. It was replaced by a Tier 2 method for cropland remaining cropland (considering effects of crop rotation, tillage, crop residues and manure inputs) and a Tier 1 method for grassland remaining grassland (considering effects of grassland management practice). 2) Evaluation of the emission factor used for organic soil and the area estimate. A review of Scandinavian literature did not support changing the emission factor value but the areas of cultivated organic soils were re-defined under cropland and grassland. 3) A Tier 1 methodology that can be used to estimate soil carbon stock changes on land-use conversion to grassland and cropland as well as all other land-use change conversion. 4) Uncertainty estimation for all source/sink categories are presented including the use of IPCC default uncertainty estimates when relevant.

Abstract

Monitoring changes in soil organic carbon (SOC) is not only linked to atmospheric CO2 dynamics, but also to the sustainability of agricultural systems, maintaining food security, reducing water pollution and soil erosion. In accordance with the methodology of the Intergovernmental Panel for Climate Change (IPCC), we developed a Tier 2 method for estimating CO2 emissions from cropland on mineral soils in Norway and compared the results with those of a Tier 1 method. As in most countries, long-term C stock or emission data sets useful for generating factors are scarce in Norway. We used a soil C balance model (ICBM) to calculate country-specific C stock change factors for relevant management systems. Agricultural activity data for 31 agrozones, from 58 ºN to 71ºN, was applied to estimate annual net CO2 emissions from 1999 to 2009. Calculated annual net emissions were larger when estimated by the Tier 2 method than Tier 1 because i) Tier 2-generated stock change factors for crop rotations with animal manure application were larger than the Tier 1 default values and ii) major changes in agricultural management during the inventory period led to a reduction in manure availability. We conclude that model-based Tier 2 methods are promising when empirical data are limited, but activity data, especially regarding animal manure practices (application rates and crop rotation preferences) are crucial for emission estimates by the IPCC methods.