An inventory of global N2O emissions from the soils of natural terrestrial ecosystems (original) (raw)
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Global Change Biology
Our understanding and quantification of global soil nitrous oxide (N 2 O) emissions and the underlying processes remain largely uncertain. Here we assessed the effects of multiple anthropogenic and natural factors, including nitrogen fertilizer (N) application, atmospheric N deposition, manure N application, land cover change, climate change and rising atmospheric CO 2 concentration, on global soil N 2 O emissions for the period 1861-2016 using a standard simulation protocol with seven process-based terrestrial biosphere models. Results suggest global soil N 2 O emissions have increased from 6.3 ± 1.1 Tg N 2 ON yr-1 in the pre-industrial period (the 1860s) to 10.0 ± 2.0 Tg N 2 ON yr-1 in the recent decade (2007-2016). Cropland soil emissions increased from 0.3 Tg N 2 ON yr-1 to 3.3 Tg N 2 ON yr-1 over the same period, accounting for 82% of the total increase. Regionally, China, South Asia and Southeast Asia underwent rapid increases in cropland N 2 O emissions since the 1970s. However, US cropland N 2 O emissions had been relatively flat in magnitude since the 1980s, and EU cropland N 2 O emissions appear to have decreased by 14%. Soil N 2 O Accepted Article This article is protected by copyright. All rights reserved. emissions from predominantly natural ecosystems accounted for 67% of the global soil emissions in the recent decade but showed only a relatively small increase of 0.7 ± 0.5 Tg N 2 ON yr-1 (11%) since the 1860s. In the recent decade, N fertilizer application, N deposition, manure N application and climate change contributed 54%, 26%, 15% and 24%, respectively, to the total increase. Rising atmospheric CO 2 concentration reduced soil N 2 O emissions by 10% through the enhanced plant N uptake, while land cover change played a minor role. Our estimation here does not account for indirect emissions from soils and the directed emissions from excreta of grazing livestock. To address uncertainties in estimating regional and global soil N 2 O emissions, this study recommends several critical strategies for improving the process-based simulations.
Biogenic NO emissions from soils: a neural network approach
2007
A B S T R A C T Soils are considered as an important source for NO emissions, but the uncertainty in quantifying these emissions worldwide remains large due to the lack of field experiments and high variability in time and space of environmental parameters influencing NO emissions. In this study, the development of a relationship for NO flux emission from soil with pertinent environmental parameters is proposed. An Artificial Neural Network (ANN) is used to find the best non-linear regression between NO fluxes and seven environmental variables, introduced step by step: soil surface temperature, surface water filled pore space, soil temperature at depth (20-30 cm), fertilisation rate, sand percentage in the soil, pH and wind speed. The network performance is evaluated each time a new variable is introduced in the network, i.e. each variable is justified and evaluated in improving the network performance. A resulting equation linking NO flux from soil and the seven variables is proposed, and shows to perform well with measurements (R 2 = 0.71), whereas other regression models give a poor correlation coefficient between calculation and measurements (R 2 ≤ 0.12 for known algorithms used at regional or global scales). ANN algorithm is shown to be a good alternative between biogeochemical and large-scale models, for future application at regional scale.
Ecological Modelling, 2004
An artificial neural network (ANN) was used to simulate nitrous oxide (N 2 O) emissions from an intensive grassland ecosystem in New Zealand. Daily N 2 O emitted was simulated as a function of six input variables of daily rainfall, soil moisture content and temperature, soil nitrate (NO 3 −), ammonium (NH 4 +) and total inorganic nitrogen content. Results showed that the ANN was able to calibrate itself to within ±0.77% of measured N 2 O values in the training data set, and within ±2.0% of values used in the validation data set. This was well within the range of the calculated uncertainties (CV = 10-43%) of the measured N 2 O emissions in the field, and demonstrated that ANNs are a viable tool for simulating complex and highly variable biological systems.
Estimating annual N2O emissions from agricultural soils in temperate climates
Global Change Biology, 2005
The Kyoto protocol requires countries to provide national inventories for a list of greenhouse gases including N 2 O. A standard methodology proposed by the Intergovernmental Panel on Climate Change (IPCC) estimates direct N 2 O emissions from soils as a constant fraction (1.25%) of the nitrogen input. This approach is insensitive to environmental variability. A more dynamic approach is needed to establish reliable N 2 O emission inventories and to propose efficient mitigation strategies. The objective of this paper is to develop a model that allows the spatial and temporal variation in environmental conditions to be taken into account in national inventories of direct N 2 O emissions. Observed annual N 2 O emission rates are used to establish statistical relationships between N 2 O emissions, seasonal climate and nitrogen-fertilization rate. Two empirical models, MCROPS and MGRASS, were developed for croplands and grasslands. Validated with an independent data set, MCROPS shows that spring temperature and summer precipitation explain 35% of the variance in annual N 2 O emissions from croplands. In MGRASS, nitrogen-fertilization rate and winter temperature explain 48% of the variance in annual N 2 O emissions from grasslands. Using long-term climate observations , the sensitivity of the models with climate variability is estimated by comparing the year-to-year prediction of the model to the precision obtained during the validation process. MCROPS is able to capture interannual variability of N 2 O emissions from croplands. However, grassland emissions show very small interannual variations, which are too small to be detectable by MGRASS. MCROPS and MGRASS improve the statistical reliability of direct N 2 O emissions compared with the IPCC default methodology. Furthermore, the models can be used to estimate the effects of interannual variation in climate, climate change on direct N 2 O emissions from soils at the regional scale.
Comparison of N2O Emissions from Soils at Three Temperate Agricultural Sites
1997
Nitrous oxide (N 2 O) 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 N 2 O 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 N 2 O 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 N 2 O fluxes were within a factor of about 2 of the observed annual fluxes, but even when models produced similar N 2 O fluxes they often produced very different estimates of gaseous N loss as nitric oxide (NO), dinitrogen (N 2), and ammonia (NH 3). Accurate simulation of soil moisture appears to be a key requirement for reliable simulation of N 2 O 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 N 2 O flux. None of the models were able to reliably generate large pulses of N 2 O during brief winter thaws that were observed at the two German sites. All models except DNDC simulated very low N 2 O 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.
Global modeling of soil nitrous oxide emissions from natural processes
Global Biogeochemical Cycles, 2013
Nitrous oxide is an important greenhouse gas and is a major ozone‒depleting substance. To understand and quantify soil nitrous oxide emissions, we expanded the Community Land Model with coupled Carbon and Nitrogen cycles version 3.5 by inserting a module to estimate monthly varying nitrous oxide emissions between 1975 and 2008. We evaluate our soil N2O emission estimates against existing emissions inventories, other process‒based model estimates, and observations from sites in the Amazon, North America, Central America, Asia, Oceania, Africa, and in Europe. The model reproduces precipitation, soil temperature, and observations of N2O emissions well at some but not at all sites and especially not during winter in the higher latitudes. Applying this model to estimate the past 24 years of global soil N2O emissions, we find that there is a significant decrease (increase) in soil N2O emissions associated with El Niño (La Niña) events.
Biogeosciences Discussions, 2018
A group of soil microbes plays an important role in nitrogen cycling and N2O emissions from natural ecosystem soils. We developed a trait-based biogeochemical model based on an extant process-based biogeochemistry model, the Terrestrial Ecosystem Model (TEM), by incorporating the detailed microbial 10 physiological processes of nitrification. The effect of ammonia-oxidizing archaea (AOA), ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) was considered in modeling nitrification. The microbial traits including microbial biomass and density were explicitly considered. In addition, nitrogen cycling was coupled with carbon dynamics based on stoichiometry theory between carbon and nitrogen. The model was parameterized using observational data and then applied to quantifying global N2O emissions from global terrestrial ecosystem soils from 15 1990 to 2000. Our estimates of 8.7±1.6 Tg N yr-1 generally agreed with previous estimates during the study period. Tropical forests are a major emitter, accounting for 42% of the global emissions. The model was more sensitive to temperature and precipitation, and less sensitive to soil organic carbon and nitrogen contents. Compared to the model without considering the detailed microbial activities, the new model shows more variations in response to seasonal changes in climate. Our study suggests that further information on microbial diversity and eco-physiology 20 features is needed. The more specific guilds and their traits shall be considered in future soil N2O emission quantifications.
Nutrient Cycling in Agroecosystems, 1998
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.
Soil N availability, rather than N deposition, controls indirect N2O emissions
Soil Biology and Biochemistry, 2016
Ammonia volatilised and re-deposited to the landscape is an indirect N 2 O emission source. This study established a relationship between N 2 O emissions, low magnitude NH 4 deposition (0e30 kg N ha À1), and soil moisture content in two soils using in-vessel incubations. Emissions from the clay soil peaked (< 0:002 g N ½g soil À1 min À1) from 85 to 93% WFPS (water filled pore space), increasing to a plateau as remaining mineral-N increased. Peak N 2 O emissions for the sandy soil were much lower (< 5 Â 10 À5 mg N ½g soil À1 min À1) and occurred at about 60% WFPS, with an indistinct relationship with increasing resident mineral N due to the low rate of nitrification in that soil. Microbial community and respiration data indicated that the clay soil was dominated by denitrifiers and was more biologically active than the sandy soil. However, the clay soil also had substantial nitrifier communities even under peak emission conditions. A process-based mathematical denitrification model was well suited to the clay soil data where all mineral-N was assumed to be nitrified (R 2 ¼ 90%), providing a substrate for denitrification. This function was not well suited to the sandy soil where nitrification was much less complete. A prototype relationship representing mineral-N pool conversions (NO 3 À and NH 4 þ) was proposed based on time, pool concentrations, moisture relationships, and soil rate constants (preliminary testing only). A threshold for mineral-N was observed: emission of N 2 O did not occur from the clay soil for mineral-N <70 mg ðkg of soilÞ À1 , suggesting that soil N availability controls indirect N 2 O emissions. This laboratory process investigation challenges the IPCC approach which predicts indirect emissions from atmospheric N deposition alone.
Nutrient Cycling in Agroecosystems, 2006
The number of published N 2 O and NO emissions measurements is increasing steadily, providing additional information about driving factors of these emissions and allowing an improvement of statistical N-emission models. We summarized information from 1008 N 2 O and 189 NO emission measurements for agricultural fields, and 207 N 2 O and 210 NO measurements for soils under natural vegetation. The factors that significantly influence agricultural N 2 O emissions were N application rate, crop type, fertilizer type, soil organic C content, soil pH and texture, and those for NO emissions include N application rate, soil N content and climate. Compared to an earlier analysis the 20% increase in the number of N 2 O measurements for agriculture did not yield more insight or reduced uncertainty, because the representation of environmental and management conditions in agro-ecosystems did not improve, while for NO emissions the additional measurements in agricultural systems did yield a considerable improvement. N 2 O emissions from soils under natural vegetation are significantly influenced by vegetation type, soil organic C content, soil pH, bulk density and drainage, while vegetation type and soil C content are major factors for NO emissions. Statistical models of these factors were used to calculate global annual emissions from fertilized cropland (3.