Changes in the Carbon Cycle of Amazon Ecosystems During the 2010 Drought

Satellite remote sensing was combined with the NASA-CASA carbon cycle simulation model to evaluate the impact of the 2010 drought (July through September) throughout tropical South America. Results indicated that net primary production (NPP) in Amazon forest areas declined by an average of 7% in 2010 compared to 2008. This represented a loss of vegetation CO2 uptake and potential Amazon rainforest growth of nearly 0.5 Pg C in 2010. The largest overall decline in ecosystem carbon gains by land cover type was predicted for closed broadleaf forest areas of the Amazon River basin, including a large fraction of regularly flooded forest areas. Model results support the hypothesis that soil and dead wood carbon decomposition fluxes of CO2 to the atmosphere were elevated during the drought period of 2010 in periodically flooded forest areas, compared to forests outside the main river floodplains

Carbon fluxes in ecosystems of Yellowstone National Park predicted from remote sensing data and simulation modeling

<p>Abstract</p> <p>Background</p> <p>A simulation model based on remote sensing data for spatial vegetation properties has been used to estimate ecosystem carbon fluxes across Yellowstone National Park (YNP). The CASA (Carnegie Ames Stanford Approach) model was applied at a regional scale to estimate seasonal and annual carbon fluxes as net primary production (NPP) and soil respiration components. Predicted net ecosystem production (NEP) flux of CO₂is estimated from the model for carbon sinks and sources over multi-year periods that varied in climate and (wildfire) disturbance histories. Monthly Enhanced Vegetation Index (EVI) image coverages from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) instrument (from 2000 to 2006) were direct inputs to the model. New map products have been added to CASA from airborne remote sensing of coarse woody debris (CWD) in areas burned by wildfires over the past two decades.</p> <p>Results</p> <p>Model results indicated that relatively cooler and wetter summer growing seasons were the most favorable for annual plant production and net ecosystem carbon gains in representative landscapes of YNP. When summed across vegetation class areas, the predominance of evergreen forest and shrubland (sagebrush) cover was evident, with these two classes together accounting for 88% of the total annual NPP flux of 2.5 Tg C yr^-1(1 Tg = 10¹²g) for the entire Yellowstone study area from 2000-2006. Most vegetation classes were estimated as net ecosystem sinks of atmospheric CO₂on annual basis, making the entire study area a moderate net sink of about +0.13 Tg C yr^-1. This average sink value for forested lands nonetheless masks the contribution of areas burned during the 1988 wildfires, which were estimated as net sources of CO₂to the atmosphere, totaling to a NEP flux of -0.04 Tg C yr^-1for the entire burned area. Several areas burned in the 1988 wildfires were estimated to be among the lowest in overall yearly NPP, namely the Hellroaring Fire, Mink Fire, and Falls Fire areas.</p> <p>Conclusions</p> <p>Rates of recovery for burned forest areas to pre-1988 biomass levels were estimated from a unique combination of remote sensing and CASA model predictions. Ecosystem production and carbon fluxes in the Greater Yellowstone Ecosystem (GYE) result from complex interactions between climate, forest age structure, and disturbance-recovery patterns of the landscape.</p

A three-layer MRI-based head phantom for experimental validation of tES simulations

Transcranial electric stimulation (tES) is being investigated for the relief of seizures in medically refractory epilepsy patients. In a quest to optimize the electrode placement and current for improvement of the outcome, we are investigating the exploitation of the pre-stimulation planning using finite element simulations based on individual anatomy from MRI [RM1] scans. A crucial step is validating the stimulation modeling accuracy, but commercial setups for validation do not exist.Hereto, we developed a three-layer head phantom, consisting of skin, skull, and brain tissue, that captures the crucial anatomical features and provides a convenient way of verifying the induced electric fields. It also enables systematic characterization of the uncertainties and variations in conductivity and anatomy. Experiments on the three-layer phantom bridge the gap between simulations and clinical practice since they also allow for using clinical hardware and electrodes.The developed phantom consists of an agar and salt brain layer, a graphite-doped polyurethane skull, and a skin layer made from agar gel with a different conductivity. In this way the solid skull separates the two gel layers, preventing possible ion drift over the layers. The anatomy is based on the ICBM 152 linear model, an average of 152 MRI scans, which enables us to intuitively link measurements and simulations. To perform the systematic characterization experiments, hardware and software were designed in-house. This allows for stimulations and measurements on the phantom in a cheap and modular way. The designed hardware consists of a PID-controlled tES stimulator, which can deliver 4 mA with a frequency up to 100 Hz, and a four-channel differential sensing board based on the OpenBCI Ganglion board.A realistic and modular phantom expands the possibilities of preclinical tES research by providing a tool to validate electric field simulations as well as experiment with clinical hardware and anatomical variations

Terrestrial ecosystem production: A process model based on global satellite and surface data

This paper presents a modeling approach aimed at seasonal resolution of global climatic and edaphic controls on patterns of terrestrial ecosystem production and soil microbial respiration. We use satellite imagery (Advanced Very High Resolution Radiometer and International Satellite Cloud Climatology Project solar radiation), along with historical climate (monthly temperature and precipitation) and soil attributes (texture, C and N contents) from global (1°) data sets as model inputs. The Carnegie‐Ames‐Stanford approach (CASA) Biosphere model runs on a monthly time interval to simulate seasonal patterns in net plant carbon fixation, biomass and nutrient allocation, litterfall, soil nitrogen mineralization, and microbial CO2 production. The model estimate of global terrestrial net primary production is 48 Pg C yr^(−1) with a maximum light use efficiency of 0.39 g C MJ^(−1) PAR. Over 70% of terrestrial net production takes place between 30°N and 30°S latitude. Steady state pools of standing litter represent global storage of around 174 Pg C (94 and 80 Pg C in nonwoody and woody pools, respectively), whereas the pool of soil C in the top 0.3 m that is turning over on decadal time scales comprises 300 Pg C. Seasonal variations in atmospheric CO_2 concentrations from three stations in the Geophysical Monitoring for Climate Change Flask Sampling Network correlate significantly with estimated net ecosystem production values averaged over 50°–80° N, 10°–30° N, and 0°–10° N

Vrednovanje analitičkih metoda i laboratorijskih postupaka u kemijskim mjerenjima

Method validation is a key element in the establishment of reference methods and in the assessment of a laboratory’s competence in producing reliable analytical data. Hence, the scope of the term »method validation« is wide, especially if one bears in mind the role of Quality Assurance/Quality Control (QA/QC). The paper puts validation in the context of the process generating chemical information, introduces basic performance parameters included in the validation processes, and evaluates current approaches to the problem. Two cases are presented in more detail: the development of European standard for chlorophenols and its validation by a full scale collaborative trial and the intralaboratory validation of a method for ethylenethiourea (ETU) by using alternative analytical techniques.Vrednovanje metoda ključni je postupak u utvrđivanju referentnih metoda i procjeni kompetencije laboratorija da proizvodi pouzdane rezultate analiza. Stoga je značenje širine okvira ovog termina posebice važno jer valja imati na umu i ulogu osiguranja kakvoće i kontrole kakvoće. Autori stavljaju postupak vrednovanja metoda u kontekst proizvodnje kemijskoanalitičkih podataka, upoznaju čitatelja s osnovnim parametrima pri ocjeni uspješnosti te ocjenjuju trenutne pristupe ovom problemu. U dva je primjera posebna pozornost posvećena razvoju europske norme za klorofenole i njezinoj ocjeni opsežnim pokusom te unutarlaboratorijskom potvrdom metode za određivanje etilentioureje rabeći alternativne analitičke tehnike

Terrestrial ecosystem production: A process model based on global satellite and surface data

This paper presents a modeling approach aimed at seasonal resolution of global climatic and edaphic controls on patterns of terrestrial ecosystem production and soil microbial respiration. We use satellite imagery (Advanced Very High Resolution Radiometer and International Satellite Cloud Climatology Project solar radiation), along with historical climate (monthly temperature and precipitation) and soil attributes (texture, C and N contents) from global (1°) data sets as model inputs. The Carnegie‐Ames‐Stanford approach (CASA) Biosphere model runs on a monthly time interval to simulate seasonal patterns in net plant carbon fixation, biomass and nutrient allocation, litterfall, soil nitrogen mineralization, and microbial CO2 production. The model estimate of global terrestrial net primary production is 48 Pg C yr^(−1) with a maximum light use efficiency of 0.39 g C MJ^(−1) PAR. Over 70% of terrestrial net production takes place between 30°N and 30°S latitude. Steady state pools of standing litter represent global storage of around 174 Pg C (94 and 80 Pg C in nonwoody and woody pools, respectively), whereas the pool of soil C in the top 0.3 m that is turning over on decadal time scales comprises 300 Pg C. Seasonal variations in atmospheric CO_2 concentrations from three stations in the Geophysical Monitoring for Climate Change Flask Sampling Network correlate significantly with estimated net ecosystem production values averaged over 50°–80° N, 10°–30° N, and 0°–10° N

Overview of the coordinated ground-based observations of Titan during the Huygens mission

Coordinated ground-based observations of Titan were performed around or during the Huygens atmospheric probe mission at Titan on 14 January 2005, connecting the momentary in situ observations by the probe with the synoptic coverage provided by continuing ground-based programs. These observations consisted of three different categories: (1) radio telescope tracking of the Huygens signal at 2040 MHz, (2) observations of the atmosphere and surface of Titan, and (3) attempts to observe radiation emitted during the Huygens Probe entry into Titan's atmosphere. The Probe radio signal was successfully acquired by a network of terrestrial telescopes, recovering a vertical profile of wind speed in Titan's atmosphere from 140 km altitude down to the surface. Ground-based observations brought new information on atmosphere and surface properties of the largest Satumian moon. No positive detection of phenomena associated with the Probe entry was reported. This paper reviews all these measurements and highlights the achieved results. The ground-based observations, both radio and optical, are of fundamental imnortance for the interpretatinn of results from the Huygens mission

How good is good enough in DP calculations?: A case study into uncertainties involved in the DP capability prediction process

Currently there are three methods of calculating the Dynamic Positioning capability of a vessel namely: static calculations, real-time time domain simulations and fast-time time domain simulations, although the outcome of the last two should be identical. In each of these methods a set of input variables is required to perform the calculations. These inputs are not always exactly known and are therefore sometimes estimated or taken from databases. It is not always clear how big the uncertainty in these estimated inputs is, and on top of that: how big the effect on the predicted DP capability is. To be able to quantify how certain a DP capability calculation is and which input data contribute most to the output uncertainty, in this thesis the input uncertainties for both static and fast-time dynamic calculations are investigated. Each method is subdivided in three different design stages which are: conceptual design, preliminary design and as built design. For the static calculations all three design stages are evaluated but for the fast-time dynamic simulations only the as built stage is considered. The static calculation part of the analysis consists of determining the input uncertainties, the input sensitivities to the output and finally calculating the output uncertainty. The method used for this calculation assumes that either the relation between input and output is linear or can be linearised at the point of interest. The input uncertainties are calculated using historical data of the Bibby Wavemaster 1 which is the vessel used as case study throughout this thesis and is specifically designed for the purpose of servicing offshore wind farms. It is observed that the input uncertainties of the main dimensions of the vessel are clearly reducing when moving through the design stages. Furthermore it is concluded that the environmental coefficients of wind, waves and current are the most uncertain, even in the conceptual design stage where input parameters of the main dimensions of the vessel vary the most. When considering the sensitivity in the three design stages no big changes were observed, meaning that in all design stages the main dimensions of the vessel are most sensitive to the output. Finally the uncertainty in the output was evaluated were it was observed that for the as built stage still a standard deviation of 4% uncertainty of the output is present, resulting in a calculated 99.7% confidence interval of either 12% too high or too low.In the dynamic calculation part only the as built stage is considered. Again the uncertain parameters are defined but due to the PID controller in the dynamic model some new input parameters are now present. The gains of this PID controller are assumed to be uncertain and are therefore taken into account during the dynamic uncertainty analysis. Due to a limitation in the aNySIM licence bought by Damen it is impossible to change the wave coefficients which causes their uncertainty not to be taken into account. Since the dynamic simulations are considered to have strong non linearities and possibly even discontinuities due to thruster saturation, the calculation method used for the static part is not applicable anymore. Therefore it is decided to use Monte Carlo simulations to quantify the uncertainty in dynamic DP calculations. Due to the large computational time required to perform large amounts of Monte Carlo simulations with aNySIM, a machine learning method is used to capture the dynamic behaviour of the vessel. A small number of simulations performed by aNySIM is required to train the model which are selected using the Sobol design of experiments technique. This technique optimises the choice of the simulation points to make sure the complete space of possible inputs is covered. By using the machine learning model to obtain an output of a dynamic simulation only a fraction of a second is required instead of 17 minutes when using aNySIM. By running the Monte Carlo simulation on the created machine learning model it was observed that the 97.7% confidence interval for offset can either be calculated up to 8.7% too low or too high whereas the prediction for heading up to 23.1% too low or too high when compared to the base case. It is concluded that using DP for the purpose of people transferring by means of a "Walk To Work" bridge, uncertainties should be taken into account to reduce both safety and contractual requirements risks.<br/