Biggest Source Of Uncertainty Research Articles

Foliar water uptake in Amazonian trees: Evidence and consequences.

The absorption of atmospheric water directly into leaves enables plants to alleviate the water stress caused by low soil moisture, hydraulic resistance in the xylem and the effect of gravity on the water column, while enabling plants to scavenge small inputs of water from leaf-wetting events. By increasing the availability of water, and supplying it from the top of the canopy (in a direction facilitated by gravity), foliar uptake (FU) may be a significant process in determining how forests interact with climate, and could alter our interpretation of current metrics for hydraulic stress and sensitivity. FU has not been reported for lowland tropical rainforests; we test whether FU occurs in six common Amazonian tree genera in lowland Amazônia, and make a first estimation of its contribution to canopy-atmosphere water exchange. We demonstrate that FU occurs in all six genera and that dew-derived water may therefore be used to "pay" for some morning transpiration in the dry season. Using meteorological and canopy wetness data, coupled with empirically derived estimates of leaf conductance to FU (kfu ), we estimate that the contribution by FU to annual transpiration at this site has a median value of 8.2% (103mm/year) and an interquartile range of 3.4%-15.3%, with the biggest sources of uncertainty being kfu and the proportion of time the canopy is wet. Our results indicate that FU is likely to be a common strategy and may have significant implications for the Amazon carbon budget. The process of foliar water uptake may also have a profound impact on the drought tolerance of individual Amazonian trees and tree species, and on the cycling of water and carbon, regionally and globally.

UNCERTAINTY ESTIMATION FOR THE MEASUREMENT OF ELECTROLYTIC CONDUCTIVITY BY SECONDARY METHOD USING CELL TYPE D

Electrolytic conductivity measurement is one of the important parameters to evaluate the concentration of dissolved substances in a solution and has been widely used in various fields of life, science, and technology. It was being used for process control and quality assurance. The traceability of the measuring results is a crucial requirement to guarantee reliability. The electrochemistry laboratory - chemical metrology Indonesia has started to develop a secondary method for electrolytic conductivity measurement. The objective of this work is to provide a traceability chain for electrolytic conductivity measurement in Indonesia through secondary reference material of Potassium Chloride (KCl) 1 M. The secondary reference material of KCl 1 M can be used as a standard for calibrating the conductivity meter, especially in seawater analysis. The secondary reference material that is developed has an electrolytic conductivity value of about 111 mS/cm and was measured using cell type D from ZMK, Germany. This cell is made from glass with two platinization electrodes inside. The distance of the electrodes is 60 mm, and the diameter of the electrode is 20 mm. The uncertainty measurement was estimated according to the ISO GUM recommendation by identifying all possible uncertainty sources in the electrolytic conductivity measurement process. The result showed that the expanded uncertainty of electrolytic conductivity measurement for KCl 1 M by a secondary method using cell type D was 0.33% at k=2 for 95% confidence level with a repeatability of the measurements and cell constant as the biggest sources of uncertainty which contributed as 80% and 17%, respectively.

Wavelet Analysis of Atmospheric Aerosols as Ambient Air Pollutants

Atmospheric aerosols, also known as atmospheric particulate matter or suspended particulate matter (SPM), are microscopic solid or liquid matter suspended in Earth's atmosphere that are transported and dispersed in the atmosphere. Study of atmospheric aerosols as ambient air pollutants has attracted the great interest of scientists due to the effect on human health and its major role in climate change. Reduced lung function, development in respiratory diseases and premature death are the effect of long-term exposure to ambient air pollution. The atmospheric aerosols affect the climate of the earth by changing the amount of incoming solar radiation and outgoing terrestrial long wave radiation retained in the earth's system and their climate effects are the biggest source of uncertainty in future climate predictions. Wavelet transforms provide excellent analysis of non-stationary time series and extracts important hidden information from a signal or data set. For the data set of SPM and RSPM of Moradabad for given time interval, Haar wavelet transform of Level1 is applied. Skewness and Kurtosis parameter describes the lack of the symmetry of data and correlation coefficient between SPM and RSPM describes the linear relationship between them.

Influence of statistical uncertainty of component reliability estimations on offshore wind farm availability

Offshore wind turbine reliability, one of the industry's biggest sources of uncertainty, is the focus of the present paper. Specifically the impact of uncertain component failure distributions at constant failure rates has been investigated with respect to its implications for wind farm availability. A fully probabilistic offshore wind simulation model has been applied to quantify results; effects shown in this paper underline the significant impact that failure probability distributions have on asset performance evaluation. It was found that wind farm availability numbers may vary in the range up to 20% just by changing the distributions of failure to a different pattern; in particular those scenarios in which extensive failure accumulation occurred led to significant losses in production. Results are interpreted and discussed mainly from the viewpoint of an offshore wind farm developer, owner and operator, with implications underlined for application in state-of-the-art offshore wind O&M (Operations and Maintenance) models and simulation tools.

Predictive Inference for Big, Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

SummaryRemote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.

Limits on the fluctuating part of y-type distortion monopole from Planck and SPT results

We use the published Planck and SPT cluster catalogs [1,2] and recently published y-distortion maps [3] to put strong observational limits on the contribution of the fluctuating part of the y-type distortions to the y-distortion monopole. Our bounds are 5.4× 10−8 < ⟨ y⟩ < 2.2× 10−6. Our upper bound is a factor of 6.8 stronger than the currently best upper 95% confidence limit from COBE-FIRAS of ⟨ y⟩ <15× 10−6. In the standard cosmology, large scale structure is the only source of such distortions and our limits therefore constrain the baryonic physics involved in the formation of the large scale structure. Our lower limit, from the detected clusters in the Planck and SPT catalogs, also implies that a Pixie-like experiment should detect the y-distortion monopole at >27-σ. The biggest sources of uncertainty in our upper limit are the monopole offsets between different HFI channel maps that we estimate to be <10−6.

A 3D model to calculate water-to-air stopping power ratio in therapeutic carbon ion fields

Air-filled ionization chambers (ICs) are extensively used in the dosimetry of charged particle radiotherapy [1]. The calibration procedure of ionization chambers for the determination of absorbed dose to water, which is the standard quantity used for dose determination in external radiotherapy [2] is known as ND,w formalism. In this formalism, the readout of the chamber is converted into absorbed dose to water via two factors: the calibration factor of the chamber, and a quality factor that accounts for the specificity of the beam. The water-to-air stopping power ratio, or sw,air, is one of the main components of these quality factors, and, in the case of carbon ion beams, its biggest source of uncertainty [2]. In a previous work by our group [3], an expression was proposed to calculate sw,air for carbon ion beams at different residual ranges, based on a set of Monte Carlo calculations and experimental measurements, namely: (1) where Rres is expressed in cm and calculated using a practical range at the 50% dose level Rres(z) = R50 – z, where z is the depth in water. This expression is based on a 1D analysis of dose and sw,air distributions, which is enough to model the variations in sw,air for homogeneous dose distributions, like the ones mostly used for calibration and quality assurance (QA) purposes. However, this 1D description might be insufficient in some cases. An example of this would be treatment plan verification with a matrix of ionization chambers [4], a protocol often used in scanning-beam facilities where a patient plan is shot into a water phantom and the deposited dose is measured at several points (see Fig. ​Fig.1).1). In such a case, the residual range Rres is not defined at every point, so the application of equation (1) is not possible.

Sea-level rise and its possible impacts given a ‘beyond 4°C world’ in the twenty-first century

The range of future climate-induced sea-level rise remains highly uncertain with continued concern that large increases in the twenty-first century cannot be ruled out. The biggest source of uncertainty is the response of the large ice sheets of Greenland and west Antarctica. Based on our analysis, a pragmatic estimate of sea-level rise by 2100, for a temperature rise of 4°C or more over the same time frame, is between 0.5 m and 2 m--the probability of rises at the high end is judged to be very low, but of unquantifiable probability. However, if realized, an indicative analysis shows that the impact potential is severe, with the real risk of the forced displacement of up to 187 million people over the century (up to 2.4% of global population). This is potentially avoidable by widespread upgrade of protection, albeit rather costly with up to 0.02 per cent of global domestic product needed, and much higher in certain nations. The likelihood of protection being successfully implemented varies between regions, and is lowest in small islands, Africa and parts of Asia, and hence these regions are the most likely to see coastal abandonment. To respond to these challenges, a multi-track approach is required, which would also be appropriate if a temperature rise of less than 4°C was expected. Firstly, we should monitor sea level to detect any significant accelerations in the rate of rise in a timely manner. Secondly, we need to improve our understanding of the climate-induced processes that could contribute to rapid sea-level rise, especially the role of the two major ice sheets, to produce better models that quantify the likely future rise more precisely. Finally, responses need to be carefully considered via a combination of climate mitigation to reduce the rise and adaptation for the residual rise in sea level. In particular, long-term strategic adaptation plans for the full range of possible sea-level rise (and other change) need to be widely developed.

Estimating the uncertainty in simulating the impacts of small farm dams on streamflow regimes in South Africa

Small dams represent an important local-scale resource designed to increase water supply reliability in many parts of the world where hydrological variability is high. There is evidence that the number of farm dams has increased substantially over the last few decades. These developments can have a substantial impact on downstream flow volumes and patterns, water use and ecological functioning. The study reports on the application of a hydrological modelling approach to investigate the uncertainty associated with simulating the impacts of farm dams in several South African catchments. The focus of the study is on sensitivity analysis and the limitations of the data that would be typically available for water resources assessments. The uncertainty mainly arises from the methods and information that are available to estimate the dam properties and the water use from the dams. The impacts are not only related to the number and size of dams, but also the extent to which they are used for water supply as well as the nature of the climate and the natural hydrological regimes. The biggest source of uncertainty in South Africa appears to be associated with a lack of reliable information on volumes and patterns of water abstraction from the dams. Citation Hughes, D. A. & Mantel, S. K. (2010) Estimating the uncertainty in simulating the impacts of small farm dams on streamflow regimes in South Africa. Hydrol. Sci. J. 55(4), 578–592.

In situ radiometric dating on Mars: Investigation of the feasibility of K-Ar dating using flight-type mass and X-ray spectrometers

The absolute chronology of Mars is poorly known and, as a consequence, a key science aim is to perform accurate radiometric dating of martian geological materials. The scientific benefits of in situ radiometric dating are significant and arguably of most importance is the calibration of the martian cratering rate, similar to what has been achieved for the Moon, to reduce the large uncertainties on absolute boundary ages of martian epochs. The Beagle 2 Mars lander was capable of performing radiometric date measurements of rocks using the analyses from two instruments in its payload: (i) the X-ray Spectrometer (XRS) and (ii) the Gas Analysis Package (GAP). We have investigated the feasibility of in situ radiometric dating using the K-Ar technique employing flight-like versions of Beagle 2 instrumentation. The K-Ar ages of six terrestrial basalts were measured and compared to the ‘control’ Ar-Ar radiometric ages in the range 171–1141 Ma. The K content of each basalt was measured by the flight spare XRS and the 40Ar content using a laboratory analogue of the GAP. The K-Ar ages of five basalts broadly agreed with their corresponding Ar-Ar ages. For one final basalt, the 40Ar content was below the detection limit and so an age could not be derived. The precision of the K-Ar ages was ∼30% on average. The conclusions from this study are that careful attention must be paid to improving the analytical performance of the instruments, in particular the accuracy and detection limits. The accuracy of the K and Ar measurements are the biggest source of uncertainty in the derived K-Ar age. Having investigated the technique using flight-type planetary instrumentation, we conclude that come of the principle challenges of conducting accurate in situ radiometric dating on Mars using instruments of these types include determining the sample mass, ensuring all the argon is liberated from the sample given the maximum achievable temperature of the mass spectrometer ovens, and argon loss and non-radiogenic argon in the analysed samples.

Characteristics of an ozone deposition module II: Sensitivity analysis

An ozone deposition module is currently being developed which will allow the estimation of stomatal fluxes of ozone into a number of vegetation types. This model is designed to be linked into a regional chemical-transport model for use within the European Monitoring and Evaluation Programme (EMEP), to provide information on possible risks to vegetation across Europe. This paper investigates the sensitivity of this model to some of the important input parameters, for two land-cover classes (temperate coniferous forests, temperate cereals). Of the factors contributing to the stomatal conductance considered within this study, those controlling soil moisture seem to be the biggest source of uncertainty. As ozone damage is believed to be driven by flux into the leaf rather than by ambient concentrations, this study suggests that flux modelling may be a practical alternative to the use of the AOT40 concept which is currently used to guide assessments of vegetation risk.