Sensitivity of spruce/moss boreal forest carbon balance to seasonal anomalies in weather

A process-oriented, daily time step model of a spruce/moss boreal ecosystem simulated 1994 and 1995 productivity for a Boreal Ecosystem-Atmosphere Study site near Thompson, Manitoba. Simulated black spruce net primary productivity (NPP) was 139 g C m−2 in 1994 and 112 in 1995; feathermoss NPP was 13.0 g C m−2 in 1994 and 9.7 in 1995; decomposition was 126 g C m−2 in 1994 and 130 in 1995; net ecosystem productivity (NEP) was an uptake of 26.3 g C m−2 in 1994 and 2.5 in 1995. A very dry period for the first half of the 1995 summer was the major cause of that year\u27s lower productivity. Sensitivity simulations explored the impact of 2-month long warmer, cooler, wetter, and drier spells on ecosystem productivity. Warmer summers decreased spruce NPP, moss NPP, and NEP; cooler summers had the opposite effect. Earlier snowmelt (due to either warmer spring temperatures or reduced winter precipitation) increased moss and spruce NPP; later snowmelt had the opposite effect. The largest effect on decomposition was a 5% reduction due to a drier summer. One-month droughts (April through October) were also imposed on 1975 base year weather. Early summer droughts reduced moss annual NPP by ∼30–40%; summer droughts reduced spruce annual NPP by 10%; late summer droughts increased moss NPP by about 20% due to reduced respiration; May to September monthly droughts reduced heterotrophic respiration by about 10%. Variability in NEP was up to roughly ±35%. Finally, 1975 growing season precipitation was redistributed into frequent, small rainstorms and infrequent, large rainstorms. These changes had no effect on spruce NPP. Frequent rainstorms increased decomposition by a few percent, moss NPP by 50%, and NEP by 20%. Infrequent rainstorms decreased decomposition by 5%, moss NPP by 50% and NEP by 15%. The impact of anomalous weather patterns on productivity of this ecosystem depended on their timing during the year. Multiyear data sets are necessary to understand this behavior and test these types of models

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 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

Climate controls on temporal variability of methane flux from a poor fen in southeastern New Hampshire: Measurement and modeling

Three scales of temporal variability were present in methane (CH4) flux data collected during a 2.5 year (mid-1990–1992) study at a small, poor fen in southeastern New Hampshire. (1) There was a strong seasonality to the fluxes (high in summer); monthly average fluxes range from 21.4 mg CH4 m−2 d−1 (February 1992) to 639.0 mg CH4 m−2 d−1 (July 1991). Annual fluxes were 68.8 g CH4 m−2 (1991) and 69.8 g CH4 m−2 (1992). (2) There was interannual variability; distribution of flux intensity was very different from 1991 to 1992, particularly the timing and rapidity of the onset of higher fluxes in the spring. (3) There was a high degree of variability in CH4 flux during the warm season; four successive weekly flux rates in July 1991 were 957, 1044, 170, and 491 mg CH4 m−2 d−1. Fluxes were correlated with peat temperature (r2=0.44) but only weakly with depth to water table (r2 = 0.14 for warm season data). Warm season fluxes appeared to be suppressed by rainstorms. Along with methane flux data we present an analysis of this temporal variability in flux, using a peatland soil climate model developed for this site. The model was driven by daily air temperature, precipitation, and net radiation; it calculated daily soil temperature and moisture profiles, water table location, and ice layer thickness. Temperature profiles were generally in good agreement with field data. Depth to water table simulations were good in 1992, fair in 1990, and poor in the summer of 1991. Using model-simulated peat climate and correlations to methane flux developed from the field data, simulated methane fluxes exhibited the same three modes of temporal variability that were present in the field flux data, though the model underestimated peak fluxes in 1990 and 1991. We conclude that temporal variability in flux is significantly influenced by climate/weather variability at all three scales and that rainfall appears to suppress methane flux for at least several days at this site

Two Answers are Better than One

What powerful mathematical tool do you use everyday? If you have ever considered how much food you can eat and still leave room for dessert, or made a rough calculation of how long it will take to travel to school, you are using estimates to help guide and inform your decisions

Learning to think globally

There is a popular bumper sticker slogan which advises us to ‘Think Globally, Act Locally’. But what does it really mean to ‘think globally’? One meaning could be to try to estimate the global consequences of the things you do. Here is one way to go about this

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

Sobol\u27 sensitivity analysis of the Holocene Peat Model: What drives carbon accumulation in peatlands?

Understanding the development of northern peatlands and their carbon accumulation dynamics is crucial in order to confidently integrate northern peatlands into global carbon cycle models. To achieve this, northern peatland models are becoming increasingly complex and now include feedback processes between peat depth, decomposition, hydrology, and vegetation composition and productivity. Here we present results from a global sensitivity analysis performed to assess the behavior and parameter interaction of a peatland simulation model. A series of simulations of the Holocene Peat Model were performed with different parameter combinations in order to assess the role of parameter interactions on the simulated total carbon mass after 5000 years of peatland development. The impact of parameter uncertainty on the simulation results is highlighted, as is the importance of multiple parameter interactions. The model sensitivity indicates that peat physical properties play an important role in peat accumulation; these parameters are poorly constrained by observations and should be a focus of future research. Furthermore, the results show that autogenic processes are able to produce a wide range of peatland development behaviors independently of any external environmental changes

Modeling impacts of changes in temperature and water table on C gas fluxes in an Alaskan peatland

Northern peatlands have accumulated a large amount of organic carbon (C) in their thick peat profile. Climate change and associated variations in soil environments are expected to have significant impacts on the C balance of these ecosystems, but the magnitude is still highly uncertain. Verifying and understanding the influences of changes in environmental factors on C gas fluxes in biogeochemical models are essential for forecasting feedbacks between C gas fluxes and climate change. In this study, we applied a biogeochemical model, DeNitrification-DeComposition (DNDC), to assess impacts of air temperature (TA) and water table (WT) on C gas fluxes in an Alaskan peatland. DNDC was validated against field measurements of net ecosystem exchange of CO2 (NEE) and CH4 fluxes under manipulated surface soil temperature and WT conditions in a moderate rich fen. The validation demonstrates that DNDC was able to capture the observed impacts of the manipulations in soil environments on C gas fluxes. To investigate responses of C gas fluxes to changes in TA and soil water condition, we conducted a series of simulations with varying TA and WT. The results demonstrate that (1) uptake rates of CO2 at the site were reduced by either too colder or warmer temperatures and generally increased with increasing soil moisture; (2) CH4 emissions showed an increasing trend as TAincreased or WT rose toward the peat surface; and (3) the site could shift from a net greenhouse gas (GHG) sink into a net GHG source under some warm and/or dry conditions. A sensitivity analysis evaluated the relative importance of TA and WT to C gas fluxes. The results indicate that both TA and WT played important roles in regulating NEE and CH4 emissions and that within the investigated ranges of the variations in TA and WT, changes in WT showed a greater impact than changes in TA on NEE, CH4 fluxes, and net C gas fluxes at the study fen

Modeling Climate-Biosphere Interactions in the Boreal Forest

The overall goal of this BOREAS Program was to develop, test, and apply a model of the carbon balance of boreal forest sites with a significant groundcover component (moss or lichen). The basic question addressed with this model was: What is the sensitivity of the boreal forest carbon balance to weather variability? More specifically: What are the differences in the sensitivities of carbon gains (photosynthesis) and carbon losses (respiration) of the various components of the ecosystem? Are there different seasonalities to their sensitivities (e.g., warmer springs will have one effect, warmer summers a different effect)? What are the effects of different patterns of successive weather years (wet/dry, warm/cool)? What, for example, would be the difference in effects of two "warmer than normal" months-one with each day warmer than normal, and the other with three normal weeks and one very hot week? Due to weather variability, how "noisy" will any carbon flux or carbon pool signal be that we might use to try to detect change? The project resulted in the development of a new boreal forest ecosystem model. This model was the first model in the BOREAS project to look closely at the role of mosses in the ecosystem carbon balance, and also was the first model in the BOREAS project to look closely at interannual variability in carbon fluxes. Along with the work of many other groups, TE-19 modeling analysis pointed to the need for a second, longer field season in 1996, with particular focus on the spring and fall transitions and on ground vegetation. BOREAS groups TE-19 (Frolking), TGB-1 (Crill) & TGB-3 (Moore & Roulet) analyzed BOREAS data and other published and unpublished data to develop a relationship between peatland ecosystem productivity and incoming radiation, which is quite distinct from the upland ecosystem relationships observed in other studies