Steve Frolking

A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity

This paper describes a rain-event driven, process-oriented simulation model, DNDC, for the evolution of nitrous oxide (N2O), carbon dioxide (CO2), and dinitrogen (N2) from agricultural soils. The model consists of three submodels: thermal-hydraulic, decomposition, and denitrification. Basic climate data drive the model to produce dynamic soil temperature and moisture profiles and shifts of aerobic-anaerobic conditions. Additional input data include soil texture and biochemical properties as well as agricultural practices. Between rainfall events the decomposition of organic matter and other oxidation reactions (including nitrification) dominate, and the levels of total organic carbon, soluble carbon, and nitrate change continuously. During rainfall events, denitrification dominates and produces N2O and N2. Daily emissions of N2O and N2 are computed during each rainfall event and cumulative emissions of the gases are determined by including nitrification N2O emissions as well. Sensitivity analyses reveal that rainfall patterns strongly influence N2O emissions from soils but that soluble carbon and nitrate can be limiting factors for N2O evolution during denitrification. During a year sensitivity simulation, variations in temperature, precipitation, organic C, clay content, and pH had significant effects on denitrification rates and N2O emissions. The responses of DNDC to changes of external parameters are consistent with field and experimental results reported in the literature.

A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments

Nine soil organic models were evaluated using twelve datasets from seven long-term experiments. Datasets represented three different land-uses (grassland, arable cropping and woodland) and a range of climatic conditions within the temperate region. Different treatments (inorganic fertilizer, organic manures and different rotations) at the same site allowed the effects of differing land management to be explored. Model simulations were evaluated against the measured data and the performance of the models was compared both qualitatively and quantitatively. Not all models were able to simulate all datasets; only four attempted all. No one model performed better than all others across all datasets. The performance of each model in simulating each dataset is discussed. A comparison of the overall performance of models across all datasets reveals that the model errors of one group of models (RothC, CANDY, DNDC, CENTURY, DAISY and NCSOIL) did not differ significantly from each other. Another group (SOMM, ITE and Verberne) did not differ significantly from each other but showed significantly larger model errors than did models in the first group. Possible reasons for differences in model performance are discussed in detail.

A model of nitrous oxide evolution from soil driven by rainfall events: 2. Model applications

Simulations of nitrous oxide (N2O) and carbon dioxide (CO2) emissions from soils were carried out with a rain-event model of nitrogen and carbon cycling processes in soils (Li et al., this issue). Model simulations were compared with five field studies: a 1-month denitrification study of a fertilized grassland in England; a 2-month study of N2O emissions from a native and fertilized grassland in Colorado; a 1-year study of N2O emissions from agricultural fields on drained, organic soils in Florida; a 1-year study of CO2 emissions from a grassland in Germany; and a 1-year study of CO2 emissions from a cultivated agricultural site in Missouri. The trends and magnitude of simulated N2O (or N2O + N2) and CO2 emissions were consistent with the results obtained in field experiments. The successful simulation of nitrous oxide and carbon dioxide emissions from the wide range of soil types studied indicates that the model, DNDC, will be a useful tool for studying linkages among climate, land use, soil-atmosphere interactions, and trace gas fluxes.

Modeling carbon biogeochemistry in agricultural soils

An existing model of C and N dynamics in soils was supplemented with a plant growth submodel and cropping practice routines (fertilization, irrigation, tillage, crop rotation, and manure amendments) to study the biogeochemistry of soil carbon in arable lands. The new model was validated against field results for short-term (1–9 years) decomposition experiments, the seasonal pattern of soil CO2 respiration, and long-term (100 years) soil carbon storage dynamics. A series of sensitivity runs investigated the impact of varying agricultural practices on soil organic carbon (SOC) sequestration. The tests were simulated for corn (maize) plots over a range of soil and climate conditions typical of the United States. The largest carbon sequestration occurred with manure additions; the results were very sensitive to soil texture (more clay led to greater sequestration). Increased N fertilization generally enhanced carbon sequestration, but the results were sensitive to soil texture, initial soil carbon content, and annual precipitation. Reduced tillage also generally (but not always) increased SOC content, though the results were very sensitive to soil texture, initial SOC content, and annual precipitation. A series of long-term simulations investigated the SOC equilibrium for various agricultural practices, soil and climate conditions, and crop rotations. Equilibrium SOC content increased with decreasing temperatures, increasing clay content, enhanced N fertilization, manure amendments, and crops with higher residue yield. Time to equilibrium appears to be one hundred to several hundred years. In all cases, equilibration time was longer for increasing SOC content than for decreasing SOC content. Efforts to enhance carbon sequestration in agricultural soils would do well to focus on those specific areas and agricultural practices with the greatest potential for increasing soil carbon content.

Interannual variability in the peatland-atmosphere carbon dioxide exchange at an ombrotrophic bog

This loss was equivalent to between 30 and 70% of the net CO2 uptake during the growing season. During the first 3 years of study, the bog was an annual sink for CO2 (260 g CO2 m 2 yr 1 ). In the fourth year, with the dry summer, however, annual NEE was only 34 g CO2 m 2 yr 1 , which is not significantly different from zero. We examined the performance of a peatland carbon simulator (PCARS) model against the tower measurements of NEE and derived ecosystem respiration (ER) and photosynthesis (PSN). PCARS ER and PSN were highly correlated with tower-derived fluxes, but the model consistently overestimated both ER and PSN, with slightly poorer comparisons in the dry year. As a result of both component fluxes being overestimated, PCARS simulated the tower NEE reasonably well. Simulated decomposition and autotrophic respiration contributed about equal proportions to ER. Shrubs accounted for the greatest proportion of PSN (85%); moss PSN declined to near zero during the summer period due to surface drying. INDEX TERMS: 0315 Atmospheric Composition and Structure: Biosphere/atmosphere interactions; 1615 Global Change: Biogeochemical processes (4805); 1890 Hydrology: Wetlands; KEYWORDS: peatland, bog, net ecosystem exchange, eddy covariance, carbon dioxide

Vulnerability of high‐latitude soil organic carbon in North America to disturbance

[1] This synthesis addresses the vulnerability of the North American high‐latitude soil organic carbon (SOC) pool to climate change. Disturbances caused by climate warming in arctic, subarctic, and boreal environments can result in significant redistribution of C among major reservoirs with potential global impacts. We divide the current northern high‐latitude SOC pools into (1) near‐surface soils where SOC is affected by seasonal freeze‐thaw processes and changes in moisture status, and (2) deeper permafrost and peatland strata down to several tens of meters depth where SOC is usually not affected by short‐term changes. We address key factors (permafrost, vegetation, hydrology, paleoenvironmental history) and processes (C input, storage, decomposition, and output) responsible for the formation of the large high‐latitude SOC pool in North America and highlight how climate‐related disturbances could alter this pool’s character and size. Press disturbances of relatively slow but persistent nature such as top‐down thawing of permafrost, and changes in hydrology, microbiological communities, pedological processes, and vegetation types, as well as pulse disturbances of relatively rapid and local nature such as wildfires and thermokarst, could substantially impact SOC stocks. Ongoing climate warming in the North American high‐latitude region could result in crossing environmental thresholds, thereby accelerating press disturbances and increasingly triggering pulse disturbances and eventually affecting the C source/sink net character of northern high‐latitude soils. Finally, we assess postdisturbance feedbacks, models, and predictions for the northern high‐latitude SOC pool, and discuss data and research gaps to be addressed by future research.

Modeling Northern Peatland Decomposition and Peat Accumulation

To test the hypothesis that long-term peat accumulation is related to contemporary carbon flux dynamics, we present the Peat Decomposition Model (PDM), a new model of long-term peat accumulation. Decomposition rates of the deeper peat are directly related to observable decomposition rates of fresh vegetation litter. Plant root effects (subsurface oxygenation and fresh litter inputs) are included. PDM considers two vegetation types, vascular and nonvascular, with different decomposition rates and aboveground and belowground litter input rates. We used PDM to investigate the sensitivities of peat accumulation in bogs and fens to productivity, root:shoot ratio, tissue decomposability, root and water table depths, and climate. Warmer and wetter conditions are more conducive to peat accumulation. Bogs are more sensitive than fens to climate conditions. Cooler and drier conditions lead to the lowest peat accumulation when productivity is more temperature sensitive than decomposition rates. We also compare peat age–depth profiles to field data. With a very general parameterization, PDM fen and bog age–depth profiles were similar to data from the the most recent 5000 years at three bog cores and a fen core in eastern Canada, but they overestimated accumulation at three other bog cores in that region. The model cannot reliably predict the amount of fen peat remaining from the first few millennia of a peatlands development. This discrepancy may relate to nonanalogue, early postglacial climatic and nutrient conditions for rich-fen peat accumulation and to the fate of this fen peat material, which is overlain by a bog as the peatland evolves, a common hydroseral succession in northern peatlands. Because PDM sensitivity tests point to these possible factors, we conclude that the static model represents a framework that shows a consistent relationship between contemporary productivity and fresh-tissue decomposition rates and observed long-term peat accumulation.

Comparison of N2O emissions from soils at three temperate agricultural sites: simulations of year-round measurements by four models

Nitrous oxide (N2O) flux simulations by four models were compared with year-round field measurements from five temperate agricultural sites in three countries. The field sites included an unfertilized, semi-arid rangeland with low N2O fluxes in eastern Colorado, USA; two fertilizer treatments (urea and nitrate) on a fertilized grass ley cut for silage in Scotland; and two fertilized, cultivated crop fields in Germany where N2O loss during the winter was quite high. The models used were daily trace gas versions of the CENTURY model, DNDC, ExpertN, and the NASA-Ames version of the CASA model. These models included similar components (soil physics, decomposition, plant growth, and nitrogen transformations), but in some cases used very different algorithms for these processes. All models generated similar results for the general cycling of nitrogen through the agro-ecosystems, but simulated nitrogen trace gas fluxes were quite different. In most cases the simulated N2O fluxes were within a factor of about 2 of the observed annual fluxes, but even when models produced similar N2O fluxes they often produced very different estimates of gaseous N loss as nitric oxide (NO), dinitrogen (N2), and ammonia (NH3). Accurate simulation of soil moisture appears to be a key requirement for reliable simulation of N2O emissions. All models simulated the general pattern of low background fluxes with high fluxes following fertilization at the Scottish sites, but they could not (or were not designed to) accurately capture the observed effects of different fertilizer types on N2O flux. None of the models were able to reliably generate large pulses of N2O during brief winter thaws that were observed at the two German sites. All models except DNDC simulated very low N2O fluxes for the dry site in Colorado. The US Trace Gas Network (TRAGNET) has provided a mechanism for this model and site intercomparison. Additional intercomparisons are needed with these and other models and additional data sets; these should include both tropical agro-ecosystems and new agricultural management techniques designed for sustainability.

Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China

[1] Large-scale assessments of the potential for food production and its impact on biogeochemical cycling require the best possible information on the distribution of cropland. This information can come from ground-based agricultural census data sets and/ or spaceborne remote sensing products, both with strengths and weaknesses. Official cropland statistics for China contain much information on the distribution of crop types, but are known to significantly underestimate total cropland areas and are generally at coarse spatial resolution. Remote sensing products can provide moderate to fine spatial resolution estimates of cropland location and extent, but supply little information on crop type or management. We combined county-scale agricultural census statistics on total cropland area and sown area of 17 major crops in 1990 with a fine-resolution land-cover map derived from 1995–1996 optical remote sensing (Landsat) data to generate 0.5� resolution maps of the distribution of rice agriculture in mainland China. Agricultural census data were used to determine the fraction of crop area in each 0.5� grid cell that was in single rice and each of 10 different multicrop paddy rice rotations (e.g., winter wheat/ rice), while the remote sensing land-cover product was used to determine the spatial distribution and extent of total cropland in China. We estimate that there were 0.30 million km 2 of paddy rice cropland; 75% of this paddy land was multicropped, and 56% had two rice plantings per year. Total sown area for paddy rice was 0.47 million km 2 . Paddy rice agriculture occurred on 23% of all cultivated land in China. INDEX TERMS: 0315 Atmospheric Composition and Structure: Biosphere/atmosphere interactions; 1615 Global Change: Biogeochemical processes (4805); KEYWORDS: paddy rice, maps, China, multicropping rotation, Landsat

Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure

[1] Abrupt forest disturbances generating gaps >0.001 km 2 impact roughly 0.4–0.7 million km 2 a 1 . Fire, windstorms, logging, and shifting cultivation are dominant disturbances; minor contributors are land conversion, flooding, landslides, and avalanches. All can have substantial impacts on canopy biomass and structure. Quantifying disturbance location, extent, severity, and the fate of disturbed biomass will improve carbon budget estimates and lead to better initialization, parameterization, and/or testing of forest carbon cycle models. Spaceborne remote sensing maps large-scale forest disturbance occurrence, location, and extent, particularly with moderate- and fine-scale resolution passive optical/near-infrared (NIR) instruments. High-resolution remote sensing (e.g., 1 m passive optical/NIR, or small footprint lidar) can map crown geometry and gaps, but has rarely been systematically applied to study small-scale disturbance and natural mortality gap dynamics over large regions. Reducing uncertainty in disturbance and recovery impacts on global forest carbon balance requires quantification of (1) predisturbance forest biomass; (2) disturbance impact on standing biomass and its fate; and (3) rate of biomass accumulation during recovery. Active remote sensing data (e.g., lidar, radar) are more directly indicative of canopy biomass and many structural properties than passive instrument data; a new generation of instruments designed to generate global coverage/sampling of canopy biomass and structure can improve our ability to quantify the carbon balance of Earth’s forests. Generating a high-quality quantitative assessment of disturbance impacts on canopy biomass and structure with spaceborne remote sensing requires comprehensive, well designed, and well coordinated field programs collecting high-quality ground-based data and linkages to dynamical models that can use this information.