FIFE data analysis: Testing BIOME-BGC predictions for grasslands

The First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) was conducted in a 15 km by 15 km research area located 8 km south of Manhattan, Kansas. The site consists primarily of native tallgrass prairie mixed with gallery oak forests and croplands. The objectives of FIFE are to better understand the role of biology in controlling the interactions between the land and the atmosphere, and to determine the value of remotely sensed data for estimating climatological parameters. The goals of FIFE are twofold: the upscale integration of models, and algorithm development for satellite remote sensing. The specific objectives of the field campaigns carried out in 1987 and 1989 were the simultaneous acquisition of satellite, atmospheric, and surface data; and the understanding of the processes controlling surface energy and mass exchange. Collected data were used to study the dynamics of various ecosystem processes (photosynthesis, evaporation and transpiration, autotrophic and heterotrophic respiration, etc.). Modelling terrestrial ecosystems at scales larger than that of a homogeneous plot led to the development of simple, generalized models of biogeochemical cycles that can be accurately applied to different biomes through the use of remotely sensed data. A model was developed called BIOME-BGC (for BioGeochemical Cycles) from a coniferous forest ecosystem model, FOREST-BGC, where a biome is considered a combination of a life forms in a specified climate. A predominately C4-photosynthetic grassland is probably the most different from a coniferous forest possible, hence the FIFE site was an excellent study area for testing BIOME-BGC. The transition from an essentially one-dimensional calculation to three-dimensional, landscape scale simulations requires the introduction of such factors as meteorology, climatology, and geomorphology. By using remotely sensed geographic information data for important model inputs, process-based ecosystem simulations at a variety of scales are possible. The second objective of this study is concerned with determining the accuracy of the estimated fluxes from BIOME-BGC, when extrapolated spatially over the entire 15-km by 15-km FIFE site. To accomplish this objective, a topographically distributed map of soil depth at the FIFE site was developed. These spatially-distributed fluxes were then tested with data from aircraft by eddy-flux correlation obtained during the FIFE experiment

A daily soil temperature model based on air temperature and precipitation for continental applications

Soil temperature is a necessary component for estimating below-ground processes for continental and global carbon budgets; however, there are an insufficient number of climatic stations monitoring soil temperature. We used an 11-day running average of daily mean air temperature to estimate daily mean soil temperature at a depth of 10 cm using linear regression. This model was tested using data from 6 climate regions across the United States. Frequency analyses for 17 of 19 data sets showed that the number of days which were within a +/-3.5 degree C range centered on the measured soil temperature varied from 77 to 96%. The values of R2 between observed and final predicted soil temperatures ranged from 0.85 to 0.96 with standard errors from 1.5 to 2.9 degrees C for all 19 simulations. Changes of soil temperature under snow cover were smaller than those without snow cover. Soil temperature under vegetation cover was also simulated assuming the rate of soil warming under vegetation cover would be reduced with increasing leaf area index according to the Beer-Lambert Law. Annual soil respiration can be estimated from the predicted soil temperature with reasonable accuracy. Daily soil temperature may be predicted from daily air temperature once regional equations have been established, because weather stations in the United States can be generalized into a few regions and sites within each region may use the same equation

Remote sensing of fuel moisture content from ratios of narrow-band vegetation water and dry-matter indices

Fuel moisture content (FMC) is an important variable for predicting the occurrence and spread of wildfire. Because FMC is calculated from the ratio of canopy water content to dry-matter content, we hypothesized that FMC may be estimated by remote sensing with a ratio of a vegetation water index to a vegetation dry-matter index. Four vegetation water indices, six dry-matter indices, and the resulting water/dry-matter index ratios were calculated using simulated leaf reflectances from the PROSPECT model. Two water indices, the Normalized Difference Infrared Index (NDII) and the Normalized Difference Water Index (NDWI), were more correlated with leaf water content than with FMC, and were not correlated with leaf dry-matter content. Two dry-matter indices, the Normalized Dry Matter Index (NDMI) and a recent index (unnamed) were correlated to leaf dry matter content, were inversely correlated with FMC, and were not correlated with water content. Ratios of these water indices and these dry-matter indices were highly and consistently correlated with FMC. Ratios of other water indices with other dry-matter indices were not consistently correlated with FMC. The ratio of NDII with NDMI was strongly related to FMC by a quadratic polynomial equation with an R2 of 0.947. Spectral reflectance data were acquired for single leaves and leaf stacks of Quercus alba, Acer rubrum, and Zea mays; the relationship between FMC and NDII/NDMI had an R2 of 0.853 and was almost identical to the equation from the PROSPECT model simulations. For the SAIL model simulations, the relationship between NDII/NDMI and FMC at the canopy scale had an R2 of 0.900, but the quadratic polynomial equation differed from the equations determined from the PROSPECT simulations and spectral reflectance data. NDMI requires narrow-band sensors to measure the effect of dry matter on reflectance at 1722 nm whereas NDII may be determined with many different sensors. Therefore, monitoring FMC with NDII/NDMI requires either a new sensor or a combination of two sensors, one with high temporal resolution for monitoring water content and one with high spectral resolution for estimating dry-matter content

Global net carbon exchange and intra-annual atmospheric CO2 concentrations predicted by an Ecosystem Process Model and Three-Dimensional Atmospheric Transport Model

A generalized terrestrial ecosystem process model, BIOME-BGC (for BIOME BioGeoChemical Cycles), was used to simulate the global fluxes of CO2 resulting from photosynthesis, autotrophic respiration, and heterotrophic respiration. Daily meteorological data for the year 1987, gridded to 1° by 1°, were used to drive the model simulations. From the maximum value of the normalized difference vegetation index (NDVI) for 1987, the leaf area index for each grid cell was computed. Global NPP was estimated to be 52 Pg C, and global Rh was estimated to be 66 Pg C. Model predictions of the stable carbon isotopic ratio 13C/12C for C3 and C4 vegetation were in accord with values published in the literature, suggesting that our computations of total net photosynthesis, and thus NPP, are more reliable than Rh. For each grid cell, daily Rh was adjusted so that the annual total was equal to annual NPP, and the resulting net carbon fluxes were used as inputs to a three-dimensional atmospheric transport model (TM2) using wind data from 1987. We compared the spatial and seasonal patterns of NPP with a diagnostic NDVI model, where NPP was derived from biweekly NDVI data and Rh was tuned to fit atmospheric CO2 observations from three northern stations. To an encouraging degree, predictions from the BIOME-BGC model agreed in phase and amplitude with observed atmospheric CO2 concentrations for 20° to 55°N, the zone in which the most complete data on ecosystem processes and meteorological input data are available. However, in the tropics and high northern latitudes, disagreements between simulated and measured CO2 concentrations indicated areas where the model could be improved. We present here a methodology by which terrestrial ecosystem models can be tested globally, not by comparisons to homogeneous-plot data, but by seasonal and spatial consistency with a diagnostic NDVI model and atmospheric CO2 observations

Estimation of Carbon Sequestration by Combining Remote Sensing and Net Ecosystem Exchange Data for Northern Mixed-Grass Prairie and Sagebrush–Steppe Ecosystems

Carbon sequestration was estimated a northern mixed-grass prairie site and a sagebrush–steppe site in southeastern Wyoming using an approach that integrates remote sensing, CO2 flux measurements, and meteorological data. Net ecosystem exchange (NEE) of CO2 was measured using aircraft and ground flux techniques and was linearly related to absorbed photosynthetically active radiation (APAR). The slope of this relationship is the radiation use efficiency (ε = 0.51 g C/MJ APAR); there were no significant differences in the regression coefficients between the two sites. Furthermore, ecosystem chamber measurements of total respiration in 1998 and 1999 were used to develop a functional relationship with daily average temperature; the Q10 of the relationship was 2.2. Using the Advanced Very High Resolution radiometer. Normalized Difference Vegetation Index and meteorological data, annual gross primary production and respiration were calculated from 1995 to 1999 for the two sites. Overall, the sagebrush– steppe site was a net carbon sink, whereas the northern mixed-grass prairie site was in carbon balance. There was no significant relationship between NEE and APAR for a coniferous forest site, indicating this method for scaling up CO2 flux data may be only applicable to rangeland ecosystems. The combination of remote sensing with data from CO2 flux networks can be used to estimate carbon sequestration regionally in rangeland ecosystems

Genetic and Physiological Analysis of Iron Biofortification in Maize Kernels

BACKGROUND: Maize is a major cereal crop widely consumed in developing countries, which have a high prevalence of iron (Fe) deficiency anemia. The major cause of Fe deficiency in these countries is inadequate intake of bioavailable Fe, where poverty is a major factor. Therefore, biofortification of maize by increasing Fe concentration and or bioavailability has great potential to alleviate this deficiency. Maize is also a model system for genomic research and thus allows the opportunity for gene discovery. Here we describe an integrated genetic and physiological analysis of Fe nutrition in maize kernels, to identify loci that influence grain Fe concentration and bioavailability. METHODOLOGY: Quantitative trait locus (QTL) analysis was used to dissect grain Fe concentration (FeGC) and Fe bioavailability (FeGB) from the Intermated B73 × Mo17 (IBM) recombinant inbred (RI) population. FeGC was determined by ion coupled argon plasma emission spectroscopy (ICP). FeGB was determined by an in vitro digestion/Caco-2 cell line bioassay. CONCLUSIONS: Three modest QTL for FeGC were detected, in spite of high heritability. This suggests that FeGC is controlled by many small QTL, which may make it a challenging trait to improve by marker assisted breeding. Ten QTL for FeGB were identified and explained 54% of the variance observed in samples from a single year/location. Three of the largest FeGB QTL were isolated in sister derived lines and their effect was observed in three subsequent seasons in New York. Single season evaluations were also made at six other sites around North America, suggesting the enhancement of FeGB was not specific to our farm site. FeGB was not correlated with FeGC or phytic acid, suggesting that novel regulators of Fe nutrition are responsible for the differences observed. Our results indicate that iron biofortification of maize grain is achievable using specialized phenotyping tools and conventional plant breeding techniques

Measurement of the Z/gamma* + b-jet cross section in pp collisions at 7 TeV

The production of b jets in association with a Z/gamma* boson is studied using proton-proton collisions delivered by the LHC at a centre-of-mass energy of 7 TeV and recorded by the CMS detector. The inclusive cross section for Z/gamma* + b-jet production is measured in a sample corresponding to an integrated luminosity of 2.2 inverse femtobarns. The Z/gamma* + b-jet cross section with Z/gamma* to ll (where ll = ee or mu mu) for events with the invariant mass 60 < M(ll) < 120 GeV, at least one b jet at the hadron level with pT > 25 GeV and abs(eta) < 2.1, and a separation between the leptons and the jets of Delta R > 0.5 is found to be 5.84 +/- 0.08 (stat.) +/- 0.72 (syst.) +(0.25)/-(0.55) (theory) pb. The kinematic properties of the events are also studied and found to be in agreement with the predictions made by the MadGraph event generator with the parton shower and the hadronisation performed by PYTHIA.Comment: Submitted to the Journal of High Energy Physic

Search for new physics with same-sign isolated dilepton events with jets and missing transverse energy

A search for new physics is performed in events with two same-sign isolated leptons, hadronic jets, and missing transverse energy in the final state. The analysis is based on a data sample corresponding to an integrated luminosity of 4.98 inverse femtobarns produced in pp collisions at a center-of-mass energy of 7 TeV collected by the CMS experiment at the LHC. This constitutes a factor of 140 increase in integrated luminosity over previously published results. The observed yields agree with the standard model predictions and thus no evidence for new physics is found. The observations are used to set upper limits on possible new physics contributions and to constrain supersymmetric models. To facilitate the interpretation of the data in a broader range of new physics scenarios, information on the event selection, detector response, and efficiencies is provided.Comment: Published in Physical Review Letter

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO

Compressed representation of a partially defined integer function over multiple arguments

In OLAP (OnLine Analitical Processing) data are analysed in an n-dimensional cube. The cube may be represented as a partially defined function over n arguments. Considering that often the function is not defined everywhere, we ask: is there a known way of representing the function or the points in which it is defined, in a more compact manner than the trivial one