Quantifying N2O and CO2 emissions from subtropical pasture

Greenhouse gas emissions from a well established, unfertilized tropical grass-legume pasture were monitored over two consecutive years using high resolution automatic sampling. Nitrous oxide emissions were highest during the summer months and were highly episodic, related more to the size and distribution of rain events than WFPS alone. Mean annual emissions were significantly higher during 2008 (5.7 ± 1.0 g N2O-N/ha/day) than 2007 (3.9 ± 0.4 and g N2O-N/ha/day) despite receiving nearly 500 mm less rain. Mean CO2 (28.2 ± 1.5 kg CO2 C/ha/day) was not significantly different (P 70% indicated a threshold for soil conditions favouring denitrification. The use of automatic chambers for high resolution greenhouse gas sampling can greatly reduce emission estimation errors associated with temperature and WFPS changes

Effective field theories for strongly correlated fermions - Insights from the functional renormalization group

'There are very few things that can be proved rigorously in condensed matter physics.' These famous words, brought to us by Nobel laureate Anthony James Leggett in 2003, summarize very well the challenging nature of problems researchers find themselves confronted with when entering the fascinating field of condensed matter physics. The former roots in the inherent many-body character of several quantum mechanical particles with modest to strong interactions between them: their individual properties might be easy to understand, while their collective behavior can be utterly complex. Strongly correlated electron systems, for example, exhibit several captivating phenomena such as superconductivity or spin-charge separation at temperatures far below the energy scale set by their mutual couplings. Moreover, the dimension of the respective Hilbert space grows exponentially, which impedes the exact diagonalization of their Hamiltonians in the thermodynamic limit. For this reason, renormalization group (RG) methods have become one of the most powerful tools of condensed matter research - scales are separated and dealt with iteratively by advancing an RG flow from the microscopic theory into the low-energy regime. In this thesis, we report on two complementary implementations of the functional renormalization group (fRG) for strongly correlated electrons. Functional RG is based on an exact hierarchy of coupled differential equations, which describe the evolution of one-particle irreducible vertices in terms of an infrared cutoff Lambda. To become amenable to numerical solutions, however, this hierarchy needs to be truncated. For sufficiently weak interactions, three-particle and higher-order vertices are irrelevant at the infrared fixed point, justifying their neglect. This one-loop approximation lays the foundation for the N-patch fRG scheme employed within the scope of this work. As an example, we study competing orders of spinless fermions on the triangular lattice, mapping out a rich phase diagram with several charge and pairing instabilities. In the strong-coupling limit, a cutting-edge implementation of the multiloop pseudofermion functional renormalization group (pffRG) for quantum spin systems at zero temperature is presented. Despite the lack of a kinetic term in the microscopic theory, we provide evidence for self-consistency of the method by demonstrating loop convergence of pseudofermion vertices, as well as robustness of susceptibility flows with respect to occupation number fluctuations around half-filling. Finally, an extension of pffRG to Hamiltonians with coupled spin and orbital degrees of freedom is discussed and results for exemplary model studies on strongly correlated electron systems are presented

Nitrous oxide emissions from irrigated cotton soils of northern Australia

An automated gas sampling methodology has been used to estimate nitrous oxide (N2O) emissions from heavy black clay soil in northern Australia where split applications of urea were applied to furrow irrigated cotton. Nitrous oxide emissions from the beds were 643 g N/ha over the 188 day measurement period (after planting), whilst the N2O emissions from the furrows were significantly higher at 967 g N/ha. The DNDC model was used to develop a full season simulation of N2O and N2 emissions. Seasonal N2O emissions were equivalent to 0.83% of applied N, with total gaseous N losses (excluding NH3) estimated to be 16% of the applied N

Urban Green and Open Spaces under Pressure: The Potential of Ecosystem Services Supply and Demand Analysis for Mediating Planning Processes in the Context of Climate Change

Climate change is a phenomenon which is discussed to be affecting cities and urbanising societies to a great extent. Thus, land use management of green and open spaces in the direction of climate protection and climate mitigation is an important aspect of sustainable urban and regional planning. However, land use planning holds the potential of causing conflicts between different stakeholders from administration, politics and civil society. The analysis of the demand of ecosystem services may therefore be a useful indicator to identify the interests of different stakeholders. Besides the demand, the analysis of the supply of ecosystem services might help to derive potential offers of climate relevant system functions and to support the planning processes of the areas of interest. Until now, the results of the analysis of ecosystem service supply and demand have been applied predominantly in regional or national contexts. For sustainable urban planning, the local level of observation thus seems to be more relevant. This study presents results of the interdisciplinary research project GREIF (Karlsruhe Institute of Technology and University of Heidelberg, Germany). It aims at identifying ecological and socio-cultural potentials of local urban green and open areas in the Rhine-Neckar metropolitan region, Germany, using an ecosystem service supply and demand approach. Thereby, six ecosystem services of the categories provisioning, regulating and cultural services are analysed for three predefined urban areas. Furthermore, the demand of ecosystem services of local residents as direct users of these areas is determined by conducting comprehensive surveys. The study focuses on the comparison of quantitative supply and qualitative demand data in order to identify discrepancies between supply and demand of ecosystem services. The results will be communicated to administrative bodies and political authorities of the region to enable the integration of additional knowledge into planning decisions. Preliminary results indicate that there are particular differences between the supply and demand of ecosystem services that affect the local residents in a direct way. Where the demand of the ecosystem services food provision and biodiversity is always higher-rated than the supply implies, the ecosystem service demand of climate regulation or renewable energy sources is always lower-rated than the supply indicates. These findings suggest that by incorporating the perceived demands of further stakeholders like planners or politicians, potential conflicting interests between ecosystem service demand and supply might become even more evident. Using this additional knowledge in the early stages of planning processes in the context of climate change might thus help to mitigate conflicts between different stakeholders

Parameter-induced uncertainty quantification of soil N 2 O, NO and CO 2 emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model

Assessing the uncertainties of simulation results of ecological models is becoming increasingly important, specifically if these models are used to estimate greenhouse gas emissions on site to regional/national levels. Four general sources of uncertainty effect the outcome of process-based models: (i) uncertainty of information used to initialise and drive the model, (ii) uncertainty of model parameters describing specific ecosystem processes, (iii) uncertainty of the model structure, and (iv) accurateness of measurements (e.g., soil-atmosphere greenhouse gas exchange) which are used for model testing and development. The aim of our study was to assess the simulation uncertainty of the process-based biogeochemical model LandscapeDNDC. For this we set up a Bayesian framework using a Markov Chain Monte Carlo (MCMC) method, to estimate the joint model parameter distribution. Data for model testing, parameter estimation and uncertainty assessment were taken from observations of soil fluxes of nitrous oxide (N2O), nitric oxide (NO) and carbon dioxide (CO2) as observed over a 10 yr period at the spruce site of the Höglwald Forest, Germany. By running four independent Markov Chains in parallel with identical properties (except for the parameter start values), an objective criteria for chain convergence developed by Gelman et al. (2003) could be used. Our approach shows that by means of the joint parameter distribution, we were able not only to limit the parameter space and specify the probability of parameter values, but also to assess the complex dependencies among model parameters used for simulating soil C and N trace gas emissions. This helped to improve the understanding of the behaviour of the complex LandscapeDNDC model while simulating soil C and N turnover processes and associated C and N soil-atmosphere exchange. In a final step the parameter distribution of the most sensitive parameters determining soil-atmosphere C and N exchange were used to obtain the parameter-induced uncertainty of simulated N2O, NO and CO2 emissions. These were compared to observational data of an calibration set (6 yr) and an independent validation set of 4 yr. The comparison showed that most of the annual observed trace gas emissions were in the range of simulated values and were predicted with a high certainty (Root-mean-squared error (RMSE) NO: 2.4 to 18.95 g N ha−1 d−1, N2O: 0.14 to 21.12 g N ha−1 d−1, CO2: 5.4 to 11.9 kg C ha−1 d−1). However, LandscapeDNDC simulations were sometimes still limited to accurately predict observed seasonal variations in fluxes

Are Race and Ethnicity Associated with How Hard The Heart Works during Submaximal Exercise? An NHANES Study

Previous studies have indicated racial/ethnic differences in the vasodilator and blood pressure responses to exercise, but the extent to which race/ethnicity impacts how hard the heart works (i.e., rate pressure product, RPP) during a given submaximal exercise is unknown. PURPOSE: Determine the impact of race/ethnicity on the work of the heart (i.e., RPP) during submaximal exercise. METHODS: Exercise data from the 2003-2004 NHANES cohort were examined. Specifically, 189 Black Americans (51% female), 181 Mexican Americans (53% female) and 370 (56% female) White Americans completed two, 3-minutes stages of submaximal treadmill exercise. Blood pressure, heart rate and rating of perceived exercise were measured during the 3rd minute of each stage. Exercise power was calculated based upon treadmill speed and grade and body mass. One-way ANOVA was performed to determine if racial differences existed at each stage. Subsequently, ANCOVA co-varying for several confounding variables was performed. RESULTS: When exercising at approximately 50 Watts and approximately at 100 Watts, RPP’s were significantly different between all race/ethnic groups measured (P \u3c 0.05). Specifically, at ~50 Watts, Black Americans averaged a RPP of 20486 ± 4445 mmHg x bpm, White Americans averaged a RPP of 19138 ± 4077 mmHg x bpm, and Mexican Americans averaged 18015 ± 4480 mmHg x bpm. When exercising at approximately 100 watts, Black Americans averaged a RPP of 26268 ± 6289 mmHg x bpm, White Americans averaged a RPP of 24814 ± 5271 mmHg x bpm, and Mexican Americans averaged 23114 ± 5800 mmHg x bpm. The RPP was primarily driven by differences in systolic blood pressure, which was greatest among the Black Americans (PCONCLUSIONS: Race/ethnicity is related to RPP during submaximal exercise, with Black Americans showing the greatest and Mexican Americans showing the least work of the heart during submaximal exercise. Further studies are needed to determine what implications racial/ethnic differences have for cardiovascular health and physical function

Taming pseudo-fermion functional renormalization for quantum spins: Finite-temperatures and the Popov-Fedotov trick

The pseudo-fermion representation for $S=1/2$ quantum spins introduces unphysical states in the Hilbert space which can be projected out using the Popov-Fedotov trick. However, state-of-the-art implementation of the functional renormalization group method for pseudo-fermions have so far omitted the Popov-Fedotov projection. Instead, restrictions to zero temperature were made and absence of unphysical contributions to the ground-state was assumed. We question this belief by exact diagonalization of several small-system counterexamples where unphysical states do contribute to the ground state. We then introduce Popov-Fedotov projection to pseudo-fermion functional renormalization, enabling finite temperature computations with only minor technical modifications to the method. At large and intermediate temperatures, our results are perturbatively controlled and we confirm their accuracy in benchmark calculations. At lower temperatures, the accuracy degrades due to truncation errors in the hierarchy of flow equations. Interestingly, these problems cannot be alleviated by switching to the parquet approximation. We introduce the spin projection as a method-intrinsic quality check. We also show that finite temperature magnetic ordering transitions can be studied via finite-size scaling.Comment: 14 pages, 8 figures; minor clarifications, added reference