A Non-Gaussian Spatial Generalized Linear Latent Variable Model

KAUST Repository

Irincheeva, Irina; Cantoni, Eva; Genton, Marc G.

2012-01-01

We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.

A Non-Gaussian Spatial Generalized Linear Latent Variable Model

KAUST Repository

Irincheeva, Irina

2012-08-03

We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.

Modelling the effects of spatial variability on radionuclide migration

International Nuclear Information System (INIS)

1998-01-01

The NEA workshop reflect the present status in national waste management program, specifically in spatial variability and performance assessment of geologic disposal sites for deed repository system the four sessions were: Spatial Variability: Its Definition and Significance to Performance Assessment and Site Characterisation; Experience with the Modelling of Radionuclide Migration in the Presence of Spatial Variability in Various Geological Environments; New Areas for Investigation: Two Personal Views; What is Wanted and What is Feasible: Views and Future Plans in Selected Waste Management Organisations. The 26 papers presented on the four oral sessions and on the poster session have been abstracted and indexed individually for the INIS database. (R.P.)

Variability in the Precision of Children’s Spatial Working Memory

Directory of Open Access Journals (Sweden)

Elena M. Galeano Weber

2018-02-01

Full Text Available Cognitive modeling studies in adults have established that visual working memory (WM capacity depends on the representational precision, as well as its variability from moment to moment. By contrast, visuospatial WM performance in children has been typically indexed by response accuracy—a binary measure that provides less information about precision with which items are stored. Here, we aimed at identifying whether and how children’s WM performance depends on the spatial precision and its variability over time in real-world contexts. Using smartphones, 110 Grade 3 and Grade 4 students performed a spatial WM updating task three times a day in school and at home for four weeks. Measures of spatial precision (i.e., Euclidean distance between presented and reported location were used for hierarchical modeling to estimate variability of spatial precision across different time scales. Results demonstrated considerable within-person variability in spatial precision across items within trials, from trial to trial and from occasion to occasion within days and from day to day. In particular, item-to-item variability was systematically increased with memory load and lowered with higher grade. Further, children with higher precision variability across items scored lower in measures of fluid intelligence. These findings emphasize the important role of transient changes in spatial precision for the development of WM.

Damage Spreading in Spatial and Small-world Random Boolean Networks

Energy Technology Data Exchange (ETDEWEB)

Lu, Qiming [Fermilab; Teuscher, Christof [Portland State U.

2014-02-18

The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean Networks (RBNs) are commonly used a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other non-random connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the relevant component at very low connectivities ($\\bar{K} \\ll 1$) and that the critical connectivity of stability $K_s$ changes compared to random networks. At higher $\\bar{K}$, this scaling remains unchanged. We also show that the relevant component of spatially local networks scales with a power-law as the system size N increases, but with a different exponent for local and small-world networks. The scaling behaviors are obtained by finite-size scaling. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.

Polynomial chaos expansion with random and fuzzy variables

Science.gov (United States)

Jacquelin, E.; Friswell, M. I.; Adhikari, S.; Dessombz, O.; Sinou, J.-J.

2016-06-01

A dynamical uncertain system is studied in this paper. Two kinds of uncertainties are addressed, where the uncertain parameters are described through random variables and/or fuzzy variables. A general framework is proposed to deal with both kinds of uncertainty using a polynomial chaos expansion (PCE). It is shown that fuzzy variables may be expanded in terms of polynomial chaos when Legendre polynomials are used. The components of the PCE are a solution of an equation that does not depend on the nature of uncertainty. Once this equation is solved, the post-processing of the data gives the moments of the random response when the uncertainties are random or gives the response interval when the variables are fuzzy. With the PCE approach, it is also possible to deal with mixed uncertainty, when some parameters are random and others are fuzzy. The results provide a fuzzy description of the response statistical moments.

Generating variable and random schedules of reinforcement using Microsoft Excel macros.

Science.gov (United States)

Bancroft, Stacie L; Bourret, Jason C

2008-01-01

Variable reinforcement schedules are used to arrange the availability of reinforcement following varying response ratios or intervals of time. Random reinforcement schedules are subtypes of variable reinforcement schedules that can be used to arrange the availability of reinforcement at a constant probability across number of responses or time. Generating schedule values for variable and random reinforcement schedules can be difficult. The present article describes the steps necessary to write macros in Microsoft Excel that will generate variable-ratio, variable-interval, variable-time, random-ratio, random-interval, and random-time reinforcement schedule values.

Spatial Variability of Soil-Water Storage in the Southern Sierra Critical Zone Observatory: Measurement and Prediction

Science.gov (United States)

Oroza, C.; Bales, R. C.; Zheng, Z.; Glaser, S. D.

2017-12-01

Predicting the spatial distribution of soil moisture in mountain environments is confounded by multiple factors, including complex topography, spatial variably of soil texture, sub-surface flow paths, and snow-soil interactions. While remote-sensing tools such as passive-microwave monitoring can measure spatial variability of soil moisture, they only capture near-surface soil layers. Large-scale sensor networks are increasingly providing soil-moisture measurements at high temporal resolution across a broader range of depths than are accessible from remote sensing. It may be possible to combine these in-situ measurements with high-resolution LIDAR topography and canopy cover to estimate the spatial distribution of soil moisture at high spatial resolution at multiple depths. We study the feasibility of this approach using six years (2009-2014) of daily volumetric water content measurements at 10-, 30-, and 60-cm depths from the Southern Sierra Critical Zone Observatory. A non-parametric, multivariate regression algorithm, Random Forest, was used to predict the spatial distribution of depth-integrated soil-water storage, based on the in-situ measurements and a combination of node attributes (topographic wetness, northness, elevation, soil texture, and location with respect to canopy cover). We observe predictable patterns of predictor accuracy and independent variable ranking during the six-year study period. Predictor accuracy is highest during the snow-cover and early recession periods but declines during the dry period. Soil texture has consistently high feature importance. Other landscape attributes exhibit seasonal trends: northness peaks during the wet-up period, and elevation and topographic-wetness index peak during the recession and dry period, respectively.

Spatial variability in alluvium properties at a low-level nuclear waste site

International Nuclear Information System (INIS)

Istok, J.D.; Blout, D.O.; Barker, L.; Johnejack, K.R.; Hammermeister, D.P.

1994-01-01

Geological and statistical models for the spatial variability of soil properties are needed to predict field-scale water flow and solute transport but only limited information is currently available on unsaturated soils below the root zone. Spatial variability of selected physical and hydrologic properties was quantified for fine- and coarse-grained alluvial deposits at a low-level nuclear waste disposal site on the Nevada Test Site. Gravimetric water content (w), bulk density (ρ b ), saturated hydraulic conductivity (K a ), and particle-size distribution were determined for vertical and horizontal core specimens and bulk samples collected from 183-m-long horizontal transects in two existing waste disposal trenches located on a single alluvial fan. The transects were approximately aligned parallel and perpendicular to the principal direction of sediment transport. Properties were modeled as either normally or lognormally distributed random variables. Sample coefficients of variation were smallest for ρ b and largest for log(K a ); a weak correlation was identified between log(K a ) and the grain-size parameter d 10 . Particle-size distributions for the fine- and coarse-grained materials were different and significant differences in the natural logarithm of saturated hydraulic conductivity, log(K a ), existed between coarse and fine layers in an excavation aligned with the principal direction of alluvium deposition but not in a perpendicular direction. 37 refs., 7 figs., 11 tabs

PaCAL: A Python Package for Arithmetic Computations with Random Variables

Directory of Open Access Journals (Sweden)

Marcin Korze?

2014-05-01

Full Text Available In this paper we present PaCAL, a Python package for arithmetical computations on random variables. The package is capable of performing the four arithmetic operations: addition, subtraction, multiplication and division, as well as computing many standard functions of random variables. Summary statistics, random number generation, plots, and histograms of the resulting distributions can easily be obtained and distribution parameter ?tting is also available. The operations are performed numerically and their results interpolated allowing for arbitrary arithmetic operations on random variables following practically any probability distribution encountered in practice. The package is easy to use, as operations on random variables are performed just as they are on standard Python variables. Independence of random variables is, by default, assumed on each step but some computations on dependent random variables are also possible. We demonstrate on several examples that the results are very accurate, often close to machine precision. Practical applications include statistics, physical measurements or estimation of error distributions in scienti?c computations.

One perspective on spatial variability in geologic mapping

Science.gov (United States)

Markewich, H.W.; Cooper, S.C.

1991-01-01

This paper discusses some of the differences between geologic mapping and soil mapping, and how the resultant maps are interpreted. The role of spatial variability in geologic mapping is addressed only indirectly because in geologic mapping there have been few attempts at quantification of spatial differences. This is largely because geologic maps deal with temporal as well as spatial variability and consider time, age, and origin, as well as composition and geometry. Both soil scientists and geologists use spatial variability to delineate mappable units; however, the classification systems from which these mappable units are defined differ greatly. Mappable soil units are derived from systematic, well-defined, highly structured sets of taxonomic criteria; whereas mappable geologic units are based on a more arbitrary heirarchy of categories that integrate many features without strict values or definitions. Soil taxonomy is a sorting tool used to reduce heterogeneity between soil units. Thus at the series level, soils in any one series are relatively homogeneous because their range of properties is small and well-defined. Soil maps show the distribution of soils on the land surface. Within a map area, soils, which are often less than 2 m thick, show a direct correlation to topography and to active surface processes as well as to parent material.

Spatial scales of pollution from variable resolution satellite imaging.

Science.gov (United States)

Chudnovsky, Alexandra A; Kostinski, Alex; Lyapustin, Alexei; Koutrakis, Petros

2013-01-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not adequate for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM(2.5) as measured by the EPA ground monitoring stations was investigated at varying spatial scales. Our analysis suggested that the correlation between PM(2.5) and AOD decreased significantly as AOD resolution was degraded. This is so despite the intrinsic mismatch between PM(2.5) ground level measurements and AOD vertically integrated measurements. Furthermore, the fine resolution results indicated spatial variability in particle concentration at a sub-10 km scale. Finally, this spatial variability of AOD within the urban domain was shown to depend on PM(2.5) levels and wind speed. Copyright © 2012 Elsevier Ltd. All rights reserved.

Evaluation of spatial variability of metal bioavailability in soils using geostatistics

DEFF Research Database (Denmark)

Owsianiak, Mikolaj; Hauschild, Michael Zwicky; Rosenbaum, Ralph K.

2012-01-01

Soil properties show signifficant spatial variability at local, regional and continental scales. This is a challenge for life cycle impact assessment (LCIA) of metals, because fate, bioavailability and effect factors are controlled by environmental chemistry and can vary orders of magnitude...... is performed using ArcGIS Geostatistical Analyst. Results show that BFs of copper span a range of 6 orders of magnitude, and have signifficant spatial variability at local and continental scales. The model nugget variance is signifficantly higher than zero, suggesting the presence of spatial variability...

Variability of the raindrop size distribution at small spatial scales

Science.gov (United States)

Berne, A.; Jaffrain, J.

2010-12-01

Because of the interactions between atmospheric turbulence and cloud microphysics, the raindrop size distribution (DSD) is strongly variable in space and time. The spatial variability of the DSD at small spatial scales (below a few km) is not well documented and not well understood, mainly because of a lack of adequate measurements at the appropriate resolutions. A network of 16 disdrometers (Parsivels) has been designed and set up over EPFL campus in Lausanne, Switzerland. This network covers a typical operational weather radar pixel of 1x1 km2. The question of the significance of the variability of the DSD at such small scales is relevant for radar remote sensing of rainfall because the DSD is often assumed to be uniform within a radar sample volume and because the Z-R relationships used to convert the measured radar reflectivity Z into rain rate R are usually derived from point measurements. Thanks to the number of disdrometers, it was possible to quantify the spatial variability of the DSD at the radar pixel scale and to show that it can be significant. In this contribution, we show that the variability of the total drop concentration, of the median volume diameter and of the rain rate are significant, taking into account the sampling uncertainty associated with disdrometer measurements. The influence of this variability on the Z-R relationship can be non-negligible. Finally, the spatial structure of the DSD is quantified using a geostatistical tool, the variogram, and indicates high spatial correlation within a radar pixel.

Spatial variability of soil CO2 emission in different topographic positions

Directory of Open Access Journals (Sweden)

Liziane de Figueiredo Brito

2010-01-01

Full Text Available The spatial variability of soil CO2 emission is controlled by several properties related to the production and transport of CO2 inside the soil. Considering that soil properties are also influenced by topography, the objective of this work was to investigate the spatial variability of soil CO2 emission in three different topographic positions in an area cultivated with sugarcane, just after mechanical harvest. One location was selected on a concave-shaped form and two others on linear-shaped form (in back-slope and foot-slope. Three grids were installed, one in each location, containing 69 points and measuring 90 x 90 m each. The spatial variability of soil CO2 emission was characterized by means of semivariance. Spatial variability models derived from soil CO2 emission were exponential in the concave location while spherical models fitted better in the linear shaped areas. The degree of spatial dependence was moderate in all cases and the range of spatial dependence for the CO2 emission in the concave area was 44.5 m, higher than the mean value obtained for the linear shaped areas (20.65 m. The spatial distribution maps of soil CO2 emission indicate a higher discontinuity of emission in the linear form when compared to the concave form.

Soil variability in engineering applications

Science.gov (United States)

Vessia, Giovanna

2014-05-01

Natural geomaterials, as soils and rocks, show spatial variability and heterogeneity of physical and mechanical properties. They can be measured by in field and laboratory testing. The heterogeneity concerns different values of litho-technical parameters pertaining similar lithological units placed close to each other. On the contrary, the variability is inherent to the formation and evolution processes experienced by each geological units (homogeneous geomaterials on average) and captured as a spatial structure of fluctuation of physical property values about their mean trend, e.g. the unit weight, the hydraulic permeability, the friction angle, the cohesion, among others. The preceding spatial variations shall be managed by engineering models to accomplish reliable designing of structures and infrastructures. Materon (1962) introduced the Geostatistics as the most comprehensive tool to manage spatial correlation of parameter measures used in a wide range of earth science applications. In the field of the engineering geology, Vanmarcke (1977) developed the first pioneering attempts to describe and manage the inherent variability in geomaterials although Terzaghi (1943) already highlighted that spatial fluctuations of physical and mechanical parameters used in geotechnical designing cannot be neglected. A few years later, Mandelbrot (1983) and Turcotte (1986) interpreted the internal arrangement of geomaterial according to Fractal Theory. In the same years, Vanmarcke (1983) proposed the Random Field Theory providing mathematical tools to deal with inherent variability of each geological units or stratigraphic succession that can be resembled as one material. In this approach, measurement fluctuations of physical parameters are interpreted through the spatial variability structure consisting in the correlation function and the scale of fluctuation. Fenton and Griffiths (1992) combined random field simulation with the finite element method to produce the Random

Maximal Inequalities for Dependent Random Variables

DEFF Research Database (Denmark)

Hoffmann-Jorgensen, Jorgen

2016-01-01

Maximal inequalities play a crucial role in many probabilistic limit theorem; for instance, the law of large numbers, the law of the iterated logarithm, the martingale limit theorem and the central limit theorem. Let X-1, X-2,... be random variables with partial sums S-k = X-1 + ... + X-k. Then a......Maximal inequalities play a crucial role in many probabilistic limit theorem; for instance, the law of large numbers, the law of the iterated logarithm, the martingale limit theorem and the central limit theorem. Let X-1, X-2,... be random variables with partial sums S-k = X-1 + ... + X......-k. Then a maximal inequality gives conditions ensuring that the maximal partial sum M-n = max(1) (...

Benford's law and continuous dependent random variables

Science.gov (United States)

Becker, Thealexa; Burt, David; Corcoran, Taylor C.; Greaves-Tunnell, Alec; Iafrate, Joseph R.; Jing, Joy; Miller, Steven J.; Porfilio, Jaclyn D.; Ronan, Ryan; Samranvedhya, Jirapat; Strauch, Frederick W.; Talbut, Blaine

2018-01-01

Many mathematical, man-made and natural systems exhibit a leading-digit bias, where a first digit (base 10) of 1 occurs not 11% of the time, as one would expect if all digits were equally likely, but rather 30%. This phenomenon is known as Benford's Law. Analyzing which datasets adhere to Benford's Law and how quickly Benford behavior sets in are the two most important problems in the field. Most previous work studied systems of independent random variables, and relied on the independence in their analyses. Inspired by natural processes such as particle decay, we study the dependent random variables that emerge from models of decomposition of conserved quantities. We prove that in many instances the distribution of lengths of the resulting pieces converges to Benford behavior as the number of divisions grow, and give several conjectures for other fragmentation processes. The main difficulty is that the resulting random variables are dependent. We handle this by using tools from Fourier analysis and irrationality exponents to obtain quantified convergence rates as well as introducing and developing techniques to measure and control the dependencies. The construction of these tools is one of the major motivations of this work, as our approach can be applied to many other dependent systems. As an example, we show that the n ! entries in the determinant expansions of n × n matrices with entries independently drawn from nice random variables converges to Benford's Law.

Localization in a one-dimensional spatially correlated random potential

International Nuclear Information System (INIS)

Kasner, M.; Weller, W.

1986-01-01

The motion of an electron in a random one-dimensional spatially correlated potential is investigated. The spatial correlation is generated by a Markov chain. It is shown that the influence of the spatial correlation can be described by means of oscillating vertices usually neglected in the Berezinskii diagram technique. Correlation mainly leads to an increase of the localization length in comparison with an uncorrelated potential. However, there is a region of the parameter, where the localization decreases. (author)

Hoeffding’s Inequality for Sums of Dependent Random Variables

Czech Academy of Sciences Publication Activity Database

Pelekis, Christos; Ramon, J.

2017-01-01

Roč. 14, č. 6 (2017), č. článku 243. ISSN 1660-5446 Institutional support: RVO:67985807 Keywords : dependent random variables * Hoeffding’s inequality * k-wise independent random variables * martingale differences Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics Impact factor: 0.868, year: 2016

Spatial variability and parametric uncertainty in performance assessment models

International Nuclear Information System (INIS)

Pensado, Osvaldo; Mancillas, James; Painter, Scott; Tomishima, Yasuo

2011-01-01

The problem of defining an appropriate treatment of distribution functions (which could represent spatial variability or parametric uncertainty) is examined based on a generic performance assessment model for a high-level waste repository. The generic model incorporated source term models available in GoldSim ® , the TDRW code for contaminant transport in sparse fracture networks with a complex fracture-matrix interaction process, and a biosphere dose model known as BDOSE TM . Using the GoldSim framework, several Monte Carlo sampling approaches and transport conceptualizations were evaluated to explore the effect of various treatments of spatial variability and parametric uncertainty on dose estimates. Results from a model employing a representative source and ensemble-averaged pathway properties were compared to results from a model allowing for stochastic variation of transport properties along streamline segments (i.e., explicit representation of spatial variability within a Monte Carlo realization). We concluded that the sampling approach and the definition of an ensemble representative do influence consequence estimates. In the examples analyzed in this paper, approaches considering limited variability of a transport resistance parameter along a streamline increased the frequency of fast pathways resulting in relatively high dose estimates, while those allowing for broad variability along streamlines increased the frequency of 'bottlenecks' reducing dose estimates. On this basis, simplified approaches with limited consideration of variability may suffice for intended uses of the performance assessment model, such as evaluation of site safety. (author)

Visualization techniques for spatial probability density function data

Directory of Open Access Journals (Sweden)

Udeepta D Bordoloi

2006-01-01

Full Text Available Novel visualization methods are presented for spatial probability density function data. These are spatial datasets, where each pixel is a random variable, and has multiple samples which are the results of experiments on that random variable. We use clustering as a means to reduce the information contained in these datasets; and present two different ways of interpreting and clustering the data. The clustering methods are used on two datasets, and the results are discussed with the help of visualization techniques designed for the spatial probability data.

Sparse modeling of spatial environmental variables associated with asthma.

Science.gov (United States)

Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

Quantitative analysis of spatial variability of geotechnical parameters

Science.gov (United States)

Fang, Xing

2018-04-01

Geotechnical parameters are the basic parameters of geotechnical engineering design, while the geotechnical parameters have strong regional characteristics. At the same time, the spatial variability of geotechnical parameters has been recognized. It is gradually introduced into the reliability analysis of geotechnical engineering. Based on the statistical theory of geostatistical spatial information, the spatial variability of geotechnical parameters is quantitatively analyzed. At the same time, the evaluation of geotechnical parameters and the correlation coefficient between geotechnical parameters are calculated. A residential district of Tianjin Survey Institute was selected as the research object. There are 68 boreholes in this area and 9 layers of mechanical stratification. The parameters are water content, natural gravity, void ratio, liquid limit, plasticity index, liquidity index, compressibility coefficient, compressive modulus, internal friction angle, cohesion and SP index. According to the principle of statistical correlation, the correlation coefficient of geotechnical parameters is calculated. According to the correlation coefficient, the law of geotechnical parameters is obtained.

Two spatial light modulator system for laboratory simulation of random beam propagation in random media.

Science.gov (United States)

Wang, Fei; Toselli, Italo; Korotkova, Olga

2016-02-10

An optical system consisting of a laser source and two independent consecutive phase-only spatial light modulators (SLMs) is shown to accurately simulate a generated random beam (first SLM) after interaction with a stationary random medium (second SLM). To illustrate the range of possibilities, a recently introduced class of random optical frames is examined on propagation in free space and several weak turbulent channels with Kolmogorov and non-Kolmogorov statistics.

Effects on ground motion related to spatial variability

International Nuclear Information System (INIS)

Vanmarcke, E.H.

1987-01-01

Models of the spectral content and the space-time correlation structure of strong earthquake ground motion are combined with transient random vibration analysis to yield site-specific response spectra that can account for the effect of local spatial averaging of the ground motion across a rigid foundation of prescribed size. The methodology is presented with reference to sites in eastern North America, although the basic approach is applicable to other seismic regions provided the source and attenuation parameters are regionally adjusted. Parameters in the spatial correlation model are based on data from the SMART-I accelerograph array, and the sensitivity of response spectra reduction factors with respect to these parameters is examined. The starting point of the analysis is the Fourier amplitude spectrum of site displacement expresses as a function of earthquake source parameters and source-to-site distance. The bedrock acceleration spectral density function at a point, derived from the displacement spectrum, is modified to account for anelastic attenuation, and where appropriate, for local soil effects and/or local spatial averaging across a foundation. Transient random vibration analysis yields approximate analytical expressions for median ground motion amplitudes and median response spectra of an earthquake defined in terms of its spectral density function and strong motion duration. The methodology is illustrated for three events characterized by their m b magnitude and epicentral distance. The focus in this paper is on the stochastic response prediction methodology enabling explicit accounting for strong motion duration and the effect of local spatial averaging on response spectra. The numerical examples enable a preliminary assessment of the reduction of response spectral amplitudes attributable to local spatial averaging across rigid foundations of different sizes. 36 refs

Comparing spatial regression to random forests for large ...

Science.gov (United States)

Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. Our primary goal is predicting MMI at over 1.1 million perennial stream reaches across the USA. For spatial regression modeling, we develop two new methods to accommodate large data: (1) a procedure that estimates optimal Box-Cox transformations to linearize covariate relationships; and (2) a computationally efficient covariate selection routine that takes into account spatial autocorrelation. We show that our new methods lead to cross-validated performance similar to random forests, but that there is an advantage for spatial regression when quantifying the uncertainty of the predictions. Simulations are used to clarify advantages for each method. This research investigates different approaches for modeling and mapping national stream condition. We use MMI data from the EPA's National Rivers and Streams Assessment and predictors from StreamCat (Hill et al., 2015). Previous studies have focused on modeling the MMI condition classes (i.e., good, fair, and po

Effect of Variable Spatial Scales on USLE-GIS Computations

Science.gov (United States)

Patil, R. J.; Sharma, S. K.

2017-12-01

Use of appropriate spatial scale is very important in Universal Soil Loss Equation (USLE) based spatially distributed soil erosion modelling. This study aimed at assessment of annual rates of soil erosion at different spatial scales/grid sizes and analysing how changes in spatial scales affect USLE-GIS computations using simulation and statistical variabilities. Efforts have been made in this study to recommend an optimum spatial scale for further USLE-GIS computations for management and planning in the study area. The present research study was conducted in Shakkar River watershed, situated in Narsinghpur and Chhindwara districts of Madhya Pradesh, India. Remote Sensing and GIS techniques were integrated with Universal Soil Loss Equation (USLE) to predict spatial distribution of soil erosion in the study area at four different spatial scales viz; 30 m, 50 m, 100 m, and 200 m. Rainfall data, soil map, digital elevation model (DEM) and an executable C++ program, and satellite image of the area were used for preparation of the thematic maps for various USLE factors. Annual rates of soil erosion were estimated for 15 years (1992 to 2006) at four different grid sizes. The statistical analysis of four estimated datasets showed that sediment loss dataset at 30 m spatial scale has a minimum standard deviation (2.16), variance (4.68), percent deviation from observed values (2.68 - 18.91 %), and highest coefficient of determination (R2 = 0.874) among all the four datasets. Thus, it is recommended to adopt this spatial scale for USLE-GIS computations in the study area due to its minimum statistical variability and better agreement with the observed sediment loss data. This study also indicates large scope for use of finer spatial scales in spatially distributed soil erosion modelling.

Stochastic geometry, spatial statistics and random fields models and algorithms

CERN Document Server

2015-01-01

Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistics and random fields, this volume places a special emphasis on fundamental classes of models and algorithms as well as on their applications, for example in materials science, biology and genetics. This book has a strong focus on simulations and includes extensive codes in Matlab and R, which are widely used in the mathematical community. It can be regarded as a continuation of the recent volume 2068 of Lecture Notes in Mathematics, where other issues of stochastic geometry, spatial statistics and random fields were considered, with a focus on asymptotic methods.

Examining environmental drivers of spatial variability in aflatoxin ...

African Journals Online (AJOL)

Examining environmental drivers of spatial variability in aflatoxin accumulation in Kenyan maize: potential utility in risk prediction models. ... however, because of high sampling cost and lack of affordable and accurate analytical methods.

On the product and ratio of Bessel random variables

Directory of Open Access Journals (Sweden)

Saralees Nadarajah

2005-01-01

Full Text Available The distributions of products and ratios of random variables are of interest in many areas of the sciences. In this paper, the exact distributions of the product |XY| and the ratio |X/Y| are derived when X and Y are independent Bessel function random variables. An application of the results is provided by tabulating the associated percentage points.

Reduction of the Random Variables of the Turbulent Wind Field

DEFF Research Database (Denmark)

Sichani, Mahdi Teimouri; Nielsen, Søren R.K.

2012-01-01

.e. Importance Sampling (IS) or Subset Simulation (SS), will be deteriorated on problems with many random variables. The problem with PDEM is that a multidimensional integral has to be carried out over the space defined by the random variables of the system. The numerical procedure requires discretization......Applicability of the Probability Density Evolution Method (PDEM) for realizing evolution of the probability density for the wind turbines has rather strict bounds on the basic number of the random variables involved in the model. The efficiency of most of the Advanced Monte Carlo (AMC) methods, i...... of the integral domain; this becomes increasingly difficult as the dimensions of the integral domain increase. On the other hand efficiency of the AMC methods is closely dependent on the design points of the problem. Presence of many random variables may increase the number of the design points, hence affects...

Designing neural networks that process mean values of random variables

International Nuclear Information System (INIS)

Barber, Michael J.; Clark, John W.

2014-01-01

We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence

Designing neural networks that process mean values of random variables

Energy Technology Data Exchange (ETDEWEB)

Barber, Michael J. [AIT Austrian Institute of Technology, Innovation Systems Department, 1220 Vienna (Austria); Clark, John W. [Department of Physics and McDonnell Center for the Space Sciences, Washington University, St. Louis, MO 63130 (United States); Centro de Ciências Matemáticas, Universidade de Madeira, 9000-390 Funchal (Portugal)

2014-06-13

We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence.

A spatial error model with continuous random effects and an application to growth convergence

Science.gov (United States)

Laurini, Márcio Poletti

2017-10-01

We propose a spatial error model with continuous random effects based on Matérn covariance functions and apply this model for the analysis of income convergence processes (β -convergence). The use of a model with continuous random effects permits a clearer visualization and interpretation of the spatial dependency patterns, avoids the problems of defining neighborhoods in spatial econometrics models, and allows projecting the spatial effects for every possible location in the continuous space, circumventing the existing aggregations in discrete lattice representations. We apply this model approach to analyze the economic growth of Brazilian municipalities between 1991 and 2010 using unconditional and conditional formulations and a spatiotemporal model of convergence. The results indicate that the estimated spatial random effects are consistent with the existence of income convergence clubs for Brazilian municipalities in this period.

A Variable Impacts Measurement in Random Forest for Mobile Cloud Computing

Directory of Open Access Journals (Sweden)

Jae-Hee Hur

2017-01-01

Full Text Available Recently, the importance of mobile cloud computing has increased. Mobile devices can collect personal data from various sensors within a shorter period of time and sensor-based data consists of valuable information from users. Advanced computation power and data analysis technology based on cloud computing provide an opportunity to classify massive sensor data into given labels. Random forest algorithm is known as black box model which is hardly able to interpret the hidden process inside. In this paper, we propose a method that analyzes the variable impact in random forest algorithm to clarify which variable affects classification accuracy the most. We apply Shapley Value with random forest to analyze the variable impact. Under the assumption that every variable cooperates as players in the cooperative game situation, Shapley Value fairly distributes the payoff of variables. Our proposed method calculates the relative contributions of the variables within its classification process. In this paper, we analyze the influence of variables and list the priority of variables that affect classification accuracy result. Our proposed method proves its suitability for data interpretation in black box model like a random forest so that the algorithm is applicable in mobile cloud computing environment.

Modelling the Spatial Isotope Variability of Precipitation in Syria

Energy Technology Data Exchange (ETDEWEB)

Kattan, Z.; Kattaa, B. [Department of Geology, Atomic Energy Commission of Syria (AECS), Damascus (Syrian Arab Republic)

2013-07-15

Attempts were made to model the spatial variability of environmental isotope ({sup 18}O, {sup 2}H and {sup 3}H) compositions of precipitation in syria. Rainfall samples periodically collected on a monthly basis from 16 different stations were used for processing and demonstrating the spatial distributions of these isotopes, together with those of deuterium excess (d) values. Mathematically, the modelling process was based on applying simple polynomial models that take into consideration the effects of major geographic factors (Lon.E., Lat.N., and altitude). The modelling results of spatial distribution of stable isotopes ({sup 18}O and {sup 2}H) were generally good, as shown from the high correlation coefficients (R{sup 2} = 0.7-0.8), calculated between the observed and predicted values. In the case of deuterium excess and tritium distributions, the results were most likely approximates (R{sup 2} = 0.5-0.6). Improving the simulation of spatial isotope variability probably requires the incorporation of other local meteorological factors, such as relative air humidity, precipitation amount and vapour pressure, which are supposed to play an important role in such an arid country. (author)

Partitioning the impacts of spatial and climatological rainfall variability in urban drainage modeling

Science.gov (United States)

Peleg, Nadav; Blumensaat, Frank; Molnar, Peter; Fatichi, Simone; Burlando, Paolo

2017-03-01

The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP - Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM - the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the

Spatial and temporal variability in urban fine particulate matter concentrations

International Nuclear Information System (INIS)

Levy, Jonathan I.; Hanna, Steven R.

2011-01-01

Identification of hot spots for urban fine particulate matter (PM 2.5 ) concentrations is complicated by the significant contributions from regional atmospheric transport and the dependence of spatial and temporal variability on averaging time. We focus on PM 2.5 patterns in New York City, which includes significant local sources, street canyons, and upwind contributions to concentrations. A literature synthesis demonstrates that long-term (e.g., one-year) average PM 2.5 concentrations at a small number of widely-distributed monitoring sites would not show substantial variability, whereas short-term (e.g., 1-h) average measurements with high spatial density would show significant variability. Statistical analyses of ambient monitoring data as a function of wind speed and direction reinforce the significance of regional transport but show evidence of local contributions. We conclude that current monitor siting may not adequately capture PM 2.5 variability in an urban area, especially in a mega-city, reinforcing the necessity of dispersion modeling and methods for analyzing high-resolution monitoring observations. - Highlights: →Fine particulate matter (PM 2.5 ) hot spots are hard to identify in urban areas. → Literature conclusions about PM 2.5 hot spots depend on study design and methods. → Hot spots are more likely for short-term concentrations at high spatial density. → Statistical methods illustrate local source impacts beyond regional transport. → Dispersion models and high-resolution monitors are both needed to find hot spots. - Fine particulate matter can vary spatially within large urban areas, in spite of the significant contribution from regional atmospheric transport.

Exponential Inequalities for Positively Associated Random Variables and Applications

Directory of Open Access Journals (Sweden)

Yang Shanchao

2008-01-01

Full Text Available Abstract We establish some exponential inequalities for positively associated random variables without the boundedness assumption. These inequalities improve the corresponding results obtained by Oliveira (2005. By one of the inequalities, we obtain the convergence rate for the case of geometrically decreasing covariances, which closes to the optimal achievable convergence rate for independent random variables under the Hartman-Wintner law of the iterated logarithm and improves the convergence rate derived by Oliveira (2005 for the above case.

Bias in random forest variable importance measures: Illustrations, sources and a solution

Directory of Open Access Journals (Sweden)

Hothorn Torsten

2007-01-01

Full Text Available Abstract Background Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories. Results Simulation studies are presented illustrating that, when random forest variable importance measures are used with data of varying types, the results are misleading because suboptimal predictor variables may be artificially preferred in variable selection. The two mechanisms underlying this deficiency are biased variable selection in the individual classification trees used to build the random forest on one hand, and effects induced by bootstrap sampling with replacement on the other hand. Conclusion We propose to employ an alternative implementation of random forests, that provides unbiased variable selection in the individual classification trees. When this method is applied using subsampling without replacement, the resulting variable importance measures can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale of measurement or their number of categories. The usage of both random forest algorithms and their variable importance measures in the R system for statistical computing is illustrated and

Spatial variability in the coefficient of thermal expansion induces pre-service stresses in computer models of virgin Gilsocarbon bricks

International Nuclear Information System (INIS)

Arregui-Mena, José David; Margetts, Lee; Griffiths, D.V.; Lever, Louise; Hall, Graham; Mummery, Paul M.

2015-01-01

In this paper, the authors test the hypothesis that tiny spatial variations in material properties may lead to significant pre-service stresses in virgin graphite bricks. To do this, they have customised ParaFEM, an open source parallel finite element package, adding support for stochastic thermo-mechanical analysis using the Monte Carlo Simulation method. For an Advanced Gas-cooled Reactor brick, three heating cases have been examined: a uniform temperature change; a uniform temperature gradient applied through the thickness of the brick and a simulated temperature profile from an operating reactor. Results are compared for mean and stochastic properties. These show that, for the proof-of-concept analyses carried out, the pre-service von Mises stress is around twenty times higher when spatial variability of material properties is introduced. The paper demonstrates that thermal gradients coupled with material incompatibilities may be important in the generation of stress in nuclear graphite reactor bricks. Tiny spatial variations in coefficient of thermal expansion (CTE) and Young's modulus can lead to the presence of thermal stresses in bricks that are free to expand. - Highlights: • Open source software has been modified to include random variability in CTE and Young's modulus. • The new software closely agrees with analytical solutions and commercial software. • Spatial variations in CTE and Young's modulus produce stresses that do not occur with mean values. • Material variability may induce pre-service stress in virgin graphite.

Spatial variability in the coefficient of thermal expansion induces pre-service stresses in computer models of virgin Gilsocarbon bricks

Energy Technology Data Exchange (ETDEWEB)

Arregui-Mena, José David, E-mail: jose.arreguimena@postgrad.manchester.ac.uk [School of Mechanical, Aerospace, and Civil Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL (United Kingdom); Margetts, Lee, E-mail: lee.margetts@manchester.ac.uk [School of Mechanical, Aerospace, and Civil Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL (United Kingdom); Griffiths, D.V., E-mail: d.v.griffiths@mines.edu [Colorado School of Mines, 1500 Illinois St, Golden, CO 80401 (United States); Lever, Louise, E-mail: louise.lever@manchester.ac.uk [Research Computing, University of Manchester, Oxford Road, Manchester, M13 9PL (United Kingdom); Hall, Graham, E-mail: graham.n.hall@manchester.ac.uk [School of Mechanical, Aerospace, and Civil Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL (United Kingdom); Mummery, Paul M., E-mail: paul.m.mummery@manchester.ac.uk [School of Mechanical, Aerospace, and Civil Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL (United Kingdom)

2015-10-15

In this paper, the authors test the hypothesis that tiny spatial variations in material properties may lead to significant pre-service stresses in virgin graphite bricks. To do this, they have customised ParaFEM, an open source parallel finite element package, adding support for stochastic thermo-mechanical analysis using the Monte Carlo Simulation method. For an Advanced Gas-cooled Reactor brick, three heating cases have been examined: a uniform temperature change; a uniform temperature gradient applied through the thickness of the brick and a simulated temperature profile from an operating reactor. Results are compared for mean and stochastic properties. These show that, for the proof-of-concept analyses carried out, the pre-service von Mises stress is around twenty times higher when spatial variability of material properties is introduced. The paper demonstrates that thermal gradients coupled with material incompatibilities may be important in the generation of stress in nuclear graphite reactor bricks. Tiny spatial variations in coefficient of thermal expansion (CTE) and Young's modulus can lead to the presence of thermal stresses in bricks that are free to expand. - Highlights: • Open source software has been modified to include random variability in CTE and Young's modulus. • The new software closely agrees with analytical solutions and commercial software. • Spatial variations in CTE and Young's modulus produce stresses that do not occur with mean values. • Material variability may induce pre-service stress in virgin graphite.

On tests of randomness for spatial point patterns

International Nuclear Information System (INIS)

Doguwa, S.I.

1990-11-01

New tests of randomness for spatial point patterns are introduced. These test statistics are then compared in a power study with the existing alternatives. These results of the power study suggest that one of the tests proposed is extremely powerful against both aggregated and regular alternatives. (author). 9 refs, 7 figs, 3 tabs

SPATIAL MODELLING FOR DESCRIBING SPATIAL VARIABILITY OF SOIL PHYSICAL PROPERTIES IN EASTERN CROATIA

Directory of Open Access Journals (Sweden)

Igor Bogunović

2016-06-01

Full Text Available The objectives of this study were to characterize the field-scale spatial variability and test several interpolation methods to identify the best spatial predictor of penetration resistance (PR, bulk density (BD and gravimetric water content (GWC in the silty loam soil in Eastern Croatia. The measurements were made on a 25 x 25-m grid which created 40 individual grid cells. Soil properties were measured at the center of the grid cell deep 0-10 cm and 10-20 cm. Results demonstrated that PR and GWC displayed strong spatial dependence at 0-10 cm BD, while there was moderate and weak spatial dependence of PR, BD and GWC at depth of 10-20 cm. Semi-variogram analysis suggests that future sampling intervals for investigated parameters can be increased to 35 m in order to reduce research costs. Additionally, interpolation models recorded similar root mean square values with high predictive accuracy. Results suggest that investigated properties do not have uniform interpolation method implying the need for spatial modelling in the evaluation of these soil properties in Eastern Croatia.

Characteristics of Spatial Structural Patterns and Temporal Variability of Annual Precipitation in Ningxia

Institute of Scientific and Technical Information of China (English)

无

2011-01-01

[Objective] The aim was to study the characteristics of the spatial structural patterns and temporal variability of annual precipitation in Ningxia.[Method] Using rotated empirical orthogonal function,the precipitation concentration index,wavelet analysis and Mann-Kendall rank statistic method,the characteristics of precipitation on the spatial-temporal variability and trend were analyzed by the monthly precipitation series in Ningxia during 1951-2008.[Result] In Ningxia,the spatial structural patterns of a...

Temporal and spatial variability in North Carolina piedmont stream temperature

Science.gov (United States)

J.L. Boggs; G. Sun; S.G. McNulty; W. Swartley; Treasure E.; W. Summer

2009-01-01

Understanding temporal and spatial patterns of in-stream temperature can provide useful information to managing future impacts of climate change on these systems. This study will compare temporal patterns and spatial variability of headwater in-stream temperature in six catchments in the piedmont of North Carolina in two different geological regions, Carolina slate...

Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes

Science.gov (United States)

Ghosh, Subimal; Vittal, H.; Sharma, Tarul; Karmakar, Subhankar; Kasiviswanathan, K. S.; Dhanesh, Y.; Sudheer, K. P.; Gunthe, S. S.

2016-01-01

India’s agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC) model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins. PMID:27463092

Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes.

Directory of Open Access Journals (Sweden)

Subimal Ghosh

Full Text Available India's agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins.

Spatial Variability of Wet Troposphere Delays Over Inland Water Bodies

Science.gov (United States)

Mehran, Ali; Clark, Elizabeth A.; Lettenmaier, Dennis P.

2017-11-01

Satellite radar altimetry has enabled the study of water levels in large lakes and reservoirs at a global scale. The upcoming Surface Water and Ocean Topography (SWOT) satellite mission (scheduled launch 2020) will simultaneously measure water surface extent and elevation at an unprecedented accuracy and resolution. However, SWOT retrieval accuracy will be affected by a number of factors, including wet tropospheric delay—the delay in the signal's passage through the atmosphere due to atmospheric water content. In past applications, the wet tropospheric delay over large inland water bodies has been corrected using atmospheric moisture profiles based on atmospheric reanalysis data at relatively coarse (tens to hundreds of kilometers) spatial resolution. These products cannot resolve subgrid variations in wet tropospheric delays at the spatial resolutions (of 1 km and finer) that SWOT is intended to resolve. We calculate zenith wet tropospheric delays (ZWDs) and their spatial variability from Weather Research and Forecasting (WRF) numerical weather prediction model simulations at 2.33 km spatial resolution over the southwestern U.S., with attention in particular to Sam Rayburn, Ray Hubbard, and Elephant Butte Reservoirs which have width and length dimensions that are of order or larger than the WRF spatial resolution. We find that spatiotemporal variability of ZWD over the inland reservoirs depends on climatic conditions at the reservoir location, as well as distance from ocean, elevation, and surface area of the reservoir, but that the magnitude of subgrid variability (relative to analysis and reanalysis products) is generally less than 10 mm.

Development of a localized probabilistic sensitivity method to determine random variable regional importance

International Nuclear Information System (INIS)

Millwater, Harry; Singh, Gulshan; Cortina, Miguel

2012-01-01

There are many methods to identify the important variable out of a set of random variables, i.e., “inter-variable” importance; however, to date there are no comparable methods to identify the “region” of importance within a random variable, i.e., “intra-variable” importance. Knowledge of the critical region of an input random variable (tail, near-tail, and central region) can provide valuable information towards characterizing, understanding, and improving a model through additional modeling or testing. As a result, an intra-variable probabilistic sensitivity method was developed and demonstrated for independent random variables that computes the partial derivative of a probabilistic response with respect to a localized perturbation in the CDF values of each random variable. These sensitivities are then normalized in absolute value with respect to the largest sensitivity within a distribution to indicate the region of importance. The methodology is implemented using the Score Function kernel-based method such that existing samples can be used to compute sensitivities for negligible cost. Numerical examples demonstrate the accuracy of the method through comparisons with finite difference and numerical integration quadrature estimates. - Highlights: ► Probabilistic sensitivity methodology. ► Determines the “region” of importance within random variables such as left tail, near tail, center, right tail, etc. ► Uses the Score Function approach to reuse the samples, hence, negligible cost. ► No restrictions on the random variable types or limit states.

Spatial analysis of "crazy quilts", a class of potentially random aesthetic artefacts.

Directory of Open Access Journals (Sweden)

Gesche Westphal-Fitch

Full Text Available Human artefacts in general are highly structured and often display ordering principles such as translational, reflectional or rotational symmetry. In contrast, human artefacts that are intended to appear random and non symmetrical are very rare. Furthermore, many studies show that humans find it extremely difficult to recognize or reproduce truly random patterns or sequences. Here, we attempt to model two-dimensional decorative spatial patterns produced by humans that show no obvious order. "Crazy quilts" represent a historically important style of quilt making that became popular in the 1870s, and lasted about 50 years. Crazy quilts are unusual because unlike most human artefacts, they are specifically intended to appear haphazard and unstructured. We evaluate the degree to which this intention was achieved by using statistical techniques of spatial point pattern analysis to compare crazy quilts with regular quilts from the same region and era and to evaluate the fit of various random distributions to these two quilt classes. We found that the two quilt categories exhibit fundamentally different spatial characteristics: The patch areas of crazy quilts derive from a continuous random distribution, while area distributions of regular quilts consist of Gaussian mixtures. These Gaussian mixtures derive from regular pattern motifs that are repeated and we suggest that such a mixture is a distinctive signature of human-made visual patterns. In contrast, the distribution found in crazy quilts is shared with many other naturally occurring spatial patterns. Centroids of patches in the two quilt classes are spaced differently and in general, crazy quilts but not regular quilts are well-fitted by a random Strauss process. These results indicate that, within the constraints of the quilt format, Victorian quilters indeed achieved their goal of generating random structures.

Spatial analysis of "crazy quilts", a class of potentially random aesthetic artefacts.

Science.gov (United States)

Westphal-Fitch, Gesche; Fitch, W Tecumseh

2013-01-01

Human artefacts in general are highly structured and often display ordering principles such as translational, reflectional or rotational symmetry. In contrast, human artefacts that are intended to appear random and non symmetrical are very rare. Furthermore, many studies show that humans find it extremely difficult to recognize or reproduce truly random patterns or sequences. Here, we attempt to model two-dimensional decorative spatial patterns produced by humans that show no obvious order. "Crazy quilts" represent a historically important style of quilt making that became popular in the 1870s, and lasted about 50 years. Crazy quilts are unusual because unlike most human artefacts, they are specifically intended to appear haphazard and unstructured. We evaluate the degree to which this intention was achieved by using statistical techniques of spatial point pattern analysis to compare crazy quilts with regular quilts from the same region and era and to evaluate the fit of various random distributions to these two quilt classes. We found that the two quilt categories exhibit fundamentally different spatial characteristics: The patch areas of crazy quilts derive from a continuous random distribution, while area distributions of regular quilts consist of Gaussian mixtures. These Gaussian mixtures derive from regular pattern motifs that are repeated and we suggest that such a mixture is a distinctive signature of human-made visual patterns. In contrast, the distribution found in crazy quilts is shared with many other naturally occurring spatial patterns. Centroids of patches in the two quilt classes are spaced differently and in general, crazy quilts but not regular quilts are well-fitted by a random Strauss process. These results indicate that, within the constraints of the quilt format, Victorian quilters indeed achieved their goal of generating random structures.

Optimal Quantum Spatial Search on Random Temporal Networks

Science.gov (United States)

Chakraborty, Shantanav; Novo, Leonardo; Di Giorgio, Serena; Omar, Yasser

2017-12-01

To investigate the performance of quantum information tasks on networks whose topology changes in time, we study the spatial search algorithm by continuous time quantum walk to find a marked node on a random temporal network. We consider a network of n nodes constituted by a time-ordered sequence of Erdös-Rényi random graphs G (n ,p ), where p is the probability that any two given nodes are connected: After every time interval τ , a new graph G (n ,p ) replaces the previous one. We prove analytically that, for any given p , there is always a range of values of τ for which the running time of the algorithm is optimal, i.e., O (√{n }), even when search on the individual static graphs constituting the temporal network is suboptimal. On the other hand, there are regimes of τ where the algorithm is suboptimal even when each of the underlying static graphs are sufficiently connected to perform optimal search on them. From this first study of quantum spatial search on a time-dependent network, it emerges that the nontrivial interplay between temporality and connectivity is key to the algorithmic performance. Moreover, our work can be extended to establish high-fidelity qubit transfer between any two nodes of the network. Overall, our findings show that one can exploit temporality to achieve optimal quantum information tasks on dynamical random networks.

Optimal Quantum Spatial Search on Random Temporal Networks.

Science.gov (United States)

Chakraborty, Shantanav; Novo, Leonardo; Di Giorgio, Serena; Omar, Yasser

2017-12-01

To investigate the performance of quantum information tasks on networks whose topology changes in time, we study the spatial search algorithm by continuous time quantum walk to find a marked node on a random temporal network. We consider a network of n nodes constituted by a time-ordered sequence of Erdös-Rényi random graphs G(n,p), where p is the probability that any two given nodes are connected: After every time interval τ, a new graph G(n,p) replaces the previous one. We prove analytically that, for any given p, there is always a range of values of τ for which the running time of the algorithm is optimal, i.e., O(sqrt[n]), even when search on the individual static graphs constituting the temporal network is suboptimal. On the other hand, there are regimes of τ where the algorithm is suboptimal even when each of the underlying static graphs are sufficiently connected to perform optimal search on them. From this first study of quantum spatial search on a time-dependent network, it emerges that the nontrivial interplay between temporality and connectivity is key to the algorithmic performance. Moreover, our work can be extended to establish high-fidelity qubit transfer between any two nodes of the network. Overall, our findings show that one can exploit temporality to achieve optimal quantum information tasks on dynamical random networks.

The effect of short-range spatial variability on soil sampling uncertainty

Energy Technology Data Exchange (ETDEWEB)

Perk, Marcel van der [Department of Physical Geography, Utrecht University, P.O. Box 80115, 3508 TC Utrecht (Netherlands)], E-mail: m.vanderperk@geo.uu.nl; De Zorzi, Paolo; Barbizzi, Sabrina; Belli, Maria [Agenzia per la Protezione dell' Ambiente e per i Servizi Tecnici (APAT), Servizio Laboratori, Misure ed Attivita di Campo, Via di Castel Romano, 100-00128 Roma (Italy); Fajgelj, Ales; Sansone, Umberto [International Atomic Energy Agency (IAEA), Agency' s Laboratories Seibersdorf, A-1400 Vienna (Austria); Jeran, Zvonka; Jacimovic, Radojko [Jozef Stefan Institute, Jamova 39, 1000 Ljubljana (Slovenia)

2008-11-15

This paper aims to quantify the soil sampling uncertainty arising from the short-range spatial variability of elemental concentrations in the topsoils of agricultural, semi-natural, and contaminated environments. For the agricultural site, the relative standard sampling uncertainty ranges between 1% and 5.5%. For the semi-natural area, the sampling uncertainties are 2-4 times larger than in the agricultural area. The contaminated site exhibited significant short-range spatial variability in elemental composition, which resulted in sampling uncertainties of 20-30%.

The effect of short-range spatial variability on soil sampling uncertainty.

Science.gov (United States)

Van der Perk, Marcel; de Zorzi, Paolo; Barbizzi, Sabrina; Belli, Maria; Fajgelj, Ales; Sansone, Umberto; Jeran, Zvonka; Jaćimović, Radojko

2008-11-01

This paper aims to quantify the soil sampling uncertainty arising from the short-range spatial variability of elemental concentrations in the topsoils of agricultural, semi-natural, and contaminated environments. For the agricultural site, the relative standard sampling uncertainty ranges between 1% and 5.5%. For the semi-natural area, the sampling uncertainties are 2-4 times larger than in the agricultural area. The contaminated site exhibited significant short-range spatial variability in elemental composition, which resulted in sampling uncertainties of 20-30%.

SPATIAL VARIABILITY OF PEDOZEMS MECHANICAL IMPEDANCE

Directory of Open Access Journals (Sweden)

Zhukov A.V.

2013-04-01

Full Text Available We studied the spatial variability of pedozem mechanical impedance in ResearchRemediation Center of the Dnipropetrovsk State Agrarian University in Ordzhonikidze. Thestatistical distribution of the soil mechanical impedance within the studied area is characterized by deviation from the normal law in 0–10 and 30–50 cm layers from the surface. 2D and 3D modeling shows the structural design of the soil as locations of high mechanical impedance which found in the soils with less hardness.

High-speed limnology: using advanced sensors to investigate spatial variability in biogeochemistry and hydrology.

Science.gov (United States)

Crawford, John T; Loken, Luke C; Casson, Nora J; Smith, Colin; Stone, Amanda G; Winslow, Luke A

2015-01-06

Advanced sensor technology is widely used in aquatic monitoring and research. Most applications focus on temporal variability, whereas spatial variability has been challenging to document. We assess the capability of water chemistry sensors embedded in a high-speed water intake system to document spatial variability. This new sensor platform continuously samples surface water at a range of speeds (0 to >45 km h(-1)) resulting in high-density, mesoscale spatial data. These novel observations reveal previously unknown variability in physical, chemical, and biological factors in streams, rivers, and lakes. By combining multiple sensors into one platform, we were able to detect terrestrial-aquatic hydrologic connections in a small dystrophic lake, to infer the role of main-channel vs backwater nutrient processing in a large river and to detect sharp chemical changes across aquatic ecosystem boundaries in a stream/lake complex. Spatial sensor data were verified in our examples by comparing with standard lab-based measurements of selected variables. Spatial fDOM data showed strong correlation with wet chemistry measurements of DOC, and optical NO3 concentrations were highly correlated with lab-based measurements. High-frequency spatial data similar to our examples could be used to further understand aquatic biogeochemical fluxes, ecological patterns, and ecosystem processes, and will both inform and benefit from fixed-site data.

Spatial variability and Cesium-137 inventories in native forest

International Nuclear Information System (INIS)

Andrello, A.C.; Appoloni, C.R.

2004-01-01

With the nuclear fission discovery and development of nuclear weapons in 1940s, artificial radioisotopes were introduced in the environment. This contamination is due to worldwide fallout by superficial nuclear tests realized from early 1950s to late 1970s by USA, former URSS, UK, France and China. One of theses radioisotopes that have been very studied is cesium-137. Cesium-137 has a half-life of 30.2 years and its biological behavior is similar to the potassium. The behavior in soil matrix, depth distribution, spatial variability and inventories values of cesium-137 has been determinate for several regions of the world. In Brazil, some research groups have worked on this subject, but there are few works published about theses properties of cesium-137. The aim of this paper was study the depth distribution, spatial variability, and inventory of cesium-137 in native forest. Two native forests (Mata 1 and Mata UEL) were sampling in region of Londrina, PR. The results shows that there is a spatial variability of 40% for Mata 1 and 42% for Mata UEL. The depth distribution of cesium-137 for two forests presented a exponential form, characteristic to undisturbed soil. Cesium-137 inventory determinate for Mata 1 was 358 Bq m -2 and for Mata UEL was 320 Bq m -2 . (author)

Temporal Changes in the Spatial Variability of Soil Nutrients

Energy Technology Data Exchange (ETDEWEB)

Hoskinson, Reed Louis; Hess, John Richard; Alessi, Randolph Samuel

1999-07-01

This paper reports the temporal changes in the spatial variability of soil nutrient concentrations across a field during the growing season, over a four-year period. This study is part of the Site-Specific Technologies for Agriculture (SST4Ag) precision farming research project at the INEEL. Uniform fertilization did not produce a uniform increase in fertility. During the growing season, several of the nutrients and micronutrients showed increases in concentration although no additional fertilization had occurred. Potato plant uptake did not explain all of these changes. Some soil micronutrient concentrations increased above levels considered detrimental to potatoes, but the plants did not show the effects in reduced yield. All the nutrients measured changed between the last sampling in the fall and the first sampling the next spring prior to fertilization. The soil microbial community may play a major role in the temporal changes in the spatial variability of soil nutrient concentrations. These temporal changes suggest potential impact when determining fertilizer recommendations, and when evaluating the results of spatially varying fertilizer application.

New Results On the Sum of Two Generalized Gaussian Random Variables

KAUST Repository

Soury, Hamza

2015-01-01

We propose in this paper a new method to compute the characteristic function (CF) of generalized Gaussian (GG) random variable in terms of the Fox H function. The CF of the sum of two independent GG random variables is then deduced. Based on this results, the probability density function (PDF) and the cumulative distribution function (CDF) of the sum distribution are obtained. These functions are expressed in terms of the bivariate Fox H function. Next, the statistics of the distribution of the sum, such as the moments, the cumulant, and the kurtosis, are analyzed and computed. Due to the complexity of bivariate Fox H function, a solution to reduce such complexity is to approximate the sum of two independent GG random variables by one GG random variable with suitable shape factor. The approximation method depends on the utility of the system so three methods of estimate the shape factor are studied and presented.

New Results on the Sum of Two Generalized Gaussian Random Variables

KAUST Repository

Soury, Hamza

2016-01-06

We propose in this paper a new method to compute the characteristic function (CF) of generalized Gaussian (GG) random variable in terms of the Fox H function. The CF of the sum of two independent GG random variables is then deduced. Based on this results, the probability density function (PDF) and the cumulative distribution function (CDF) of the sum distribution are obtained. These functions are expressed in terms of the bivariate Fox H function. Next, the statistics of the distribution of the sum, such as the moments, the cumulant, and the kurtosis, are analyzed and computed. Due to the complexity of bivariate Fox H function, a solution to reduce such complexity is to approximate the sum of two independent GG random variables by one GG random variable with suitable shape factor. The approximation method depends on the utility of the system so three methods of estimate the shape factor are studied and presented [1].

New Results on the Sum of Two Generalized Gaussian Random Variables

KAUST Repository

Soury, Hamza; Alouini, Mohamed-Slim

2016-01-01

We propose in this paper a new method to compute the characteristic function (CF) of generalized Gaussian (GG) random variable in terms of the Fox H function. The CF of the sum of two independent GG random variables is then deduced. Based on this results, the probability density function (PDF) and the cumulative distribution function (CDF) of the sum distribution are obtained. These functions are expressed in terms of the bivariate Fox H function. Next, the statistics of the distribution of the sum, such as the moments, the cumulant, and the kurtosis, are analyzed and computed. Due to the complexity of bivariate Fox H function, a solution to reduce such complexity is to approximate the sum of two independent GG random variables by one GG random variable with suitable shape factor. The approximation method depends on the utility of the system so three methods of estimate the shape factor are studied and presented [1].

Relative spatial soil geochemical variability along two transects across the United States and Canada

Science.gov (United States)

Garrett, Robert G.

2009-01-01

To support the development of protocols for the proposed North American Soil Geochemical Landscapes project, whose objective is to establish baselines for the geochemistry of North American soils, two continental-scale transects across the United States and Canada were sampled in 2004. The sampling employed a spatially stratified random sampling design in order to estimate the variability between 40-km linear sampling units, within them, at sample sites, and due to sample preparation and analytical chemical procedures. The 40-km scale was chosen to be consistent with the density proposed for the continental-scale project. The two transects, north–south (N–S) from northern Manitoba to the USA–Mexico border near El Paso, Texas, and east–west (E–W) from the Virginia shore north of Washington, DC, to north of San Francisco, California, closely following the 38th parallel, have been studied individually. The purpose of this study was to determine if statistically significant systematic spatial variation occurred along the transects. Data for 38 major, minor and trace elements in A- and C-horizon soils where less than 5% of the data were below the detection limit were investigated by Analysis of Variance (ANOVA). A total of 15 elements (K, Na, As, Ba, Be, Ce, La, Mn, Nb, P, Rb, Sb, Th, Tl and W) demonstrated statistically significant (p<0.05) variability at the between-40-km scale for both horizons along both transects. Only Cu failed to demonstrate significant variability at the between-40-km scale for both soil horizons along both transects.

Stochastic Optimal Estimation with Fuzzy Random Variables and Fuzzy Kalman Filtering

Institute of Scientific and Technical Information of China (English)

FENG Yu-hu

2005-01-01

By constructing a mean-square performance index in the case of fuzzy random variable, the optimal estimation theorem for unknown fuzzy state using the fuzzy observation data are given. The state and output of linear discrete-time dynamic fuzzy system with Gaussian noise are Gaussian fuzzy random variable sequences. An approach to fuzzy Kalman filtering is discussed. Fuzzy Kalman filtering contains two parts: a real-valued non-random recurrence equation and the standard Kalman filtering.

Spatial variability in streambed hydraulic conductivity of contrasting stream morphologies

DEFF Research Database (Denmark)

Sebök, Éva; Calvache, Carlos Duque; Engesgaard, Peter Knudegaard

2015-01-01

inner bend of the stream, whereas high Kv values were observed at the erosional outer bend and near the middle of the channel. Calculated Kv values were related to the thickness of the organic streambed sediment layer and also showed higher temporal variability than Kh because of sedimentation...... small-scale measurements were taken in December 2011 and August 2012, both in a straight stream channel with homogeneous elevation and downstream of a channel meander with heterogeneous elevation. All streambed attributes showed large spatial variability. Kh values were the highest at the depositional...... and scouring processes affecting the upper layers of the streambed. Test locations at the channel bend showed a more heterogeneous distribution of streambed properties than test locations in the straight channel, whereas within the channel bend, higher spatial variability in streambed attributes was observed...

Statistics for Ratios of Rayleigh, Rician, Nakagami-m, and Weibull Distributed Random Variables

Directory of Open Access Journals (Sweden)

Dragana Č. Pavlović

2013-01-01

Full Text Available The distributions of ratios of random variables are of interest in many areas of the sciences. In this brief paper, we present the joint probability density function (PDF and PDF of maximum of ratios μ1=R1/r1 and μ2=R2/r2 for the cases where R1, R2, r1, and r2 are Rayleigh, Rician, Nakagami-m, and Weibull distributed random variables. Random variables R1 and R2, as well as random variables r1 and r2, are correlated. Ascertaining on the suitability of the Weibull distribution to describe fading in both indoor and outdoor environments, special attention is dedicated to the case of Weibull random variables. For this case, analytical expressions for the joint PDF, PDF of maximum, PDF of minimum, and product moments of arbitrary number of ratios μi=Ri/ri, i=1,…,L are obtained. Random variables in numerator, Ri, as well as random variables in denominator, ri, are exponentially correlated. To the best of the authors' knowledge, analytical expressions for the PDF of minimum and product moments of {μi}i=1L are novel in the open technical literature. The proposed mathematical analysis is complemented by various numerical results. An application of presented theoretical results is illustrated with respect to performance assessment of wireless systems.

Partial summations of stationary sequences of non-Gaussian random variables

DEFF Research Database (Denmark)

Mohr, Gunnar; Ditlevsen, Ove Dalager

1996-01-01

The distribution of the sum of a finite number of identically distributed random variables is in many cases easily determined given that the variables are independent. The moments of any order of the sum can always be expressed by the moments of the single term without computational problems...... of convergence of the distribution of a sum (or an integral) of mutually dependent random variables to the Gaussian distribution. The paper is closely related to the work in Ditlevsen el al. [Ditlevsen, O., Mohr, G. & Hoffmeyer, P. Integration of non-Gaussian fields. Prob. Engng Mech 11 (1996) 15-23](2)....... lognormal variables or polynomials of standard Gaussian variables. The dependency structure is induced by specifying the autocorrelation structure of the sequence of standard Gaussian variables. Particularly useful polynomials are the Winterstein approximations that distributionally fit with non...

Variable versus conventional lung protective mechanical ventilation during open abdominal surgery: study protocol for a randomized controlled trial.

Science.gov (United States)

Spieth, Peter M; Güldner, Andreas; Uhlig, Christopher; Bluth, Thomas; Kiss, Thomas; Schultz, Marcus J; Pelosi, Paolo; Koch, Thea; Gama de Abreu, Marcelo

2014-05-02

General anesthesia usually requires mechanical ventilation, which is traditionally accomplished with constant tidal volumes in volume- or pressure-controlled modes. Experimental studies suggest that the use of variable tidal volumes (variable ventilation) recruits lung tissue, improves pulmonary function and reduces systemic inflammatory response. However, it is currently not known whether patients undergoing open abdominal surgery might benefit from intraoperative variable ventilation. The PROtective VARiable ventilation trial ('PROVAR') is a single center, randomized controlled trial enrolling 50 patients who are planning for open abdominal surgery expected to last longer than 3 hours. PROVAR compares conventional (non-variable) lung protective ventilation (CV) with variable lung protective ventilation (VV) regarding pulmonary function and inflammatory response. The primary endpoint of the study is the forced vital capacity on the first postoperative day. Secondary endpoints include further lung function tests, plasma cytokine levels, spatial distribution of ventilation assessed by means of electrical impedance tomography and postoperative pulmonary complications. We hypothesize that VV improves lung function and reduces systemic inflammatory response compared to CV in patients receiving mechanical ventilation during general anesthesia for open abdominal surgery longer than 3 hours. PROVAR is the first randomized controlled trial aiming at intra- and postoperative effects of VV on lung function. This study may help to define the role of VV during general anesthesia requiring mechanical ventilation. Clinicaltrials.gov NCT01683578 (registered on September 3 3012).

Probabilistic and spatially variable niches inferred from demography

Science.gov (United States)

Jeffrey M. Diez; Itamar Giladi; Robert Warren; H. Ronald. Pulliam

2014-01-01

Summary 1. Mismatches between species distributions and habitat suitability are predicted by niche theory and have important implications for forecasting how species may respond to environmental changes. Quantifying these mismatches is challenging, however, due to the high dimensionality of species niches and the large spatial and temporal variability in population...

Qualitatively Assessing Randomness in SVD Results

Science.gov (United States)

Lamb, K. W.; Miller, W. P.; Kalra, A.; Anderson, S.; Rodriguez, A.

2012-12-01

Singular Value Decomposition (SVD) is a powerful tool for identifying regions of significant co-variability between two spatially distributed datasets. SVD has been widely used in atmospheric research to define relationships between sea surface temperatures, geopotential height, wind, precipitation and streamflow data for myriad regions across the globe. A typical application for SVD is to identify leading climate drivers (as observed in the wind or pressure data) for a particular hydrologic response variable such as precipitation, streamflow, or soil moisture. One can also investigate the lagged relationship between a climate variable and the hydrologic response variable using SVD. When performing these studies it is important to limit the spatial bounds of the climate variable to reduce the chance of random co-variance relationships being identified. On the other hand, a climate region that is too small may ignore climate signals which have more than a statistical relationship to a hydrologic response variable. The proposed research seeks to identify a qualitative method of identifying random co-variability relationships between two data sets. The research identifies the heterogeneous correlation maps from several past results and compares these results with correlation maps produced using purely random and quasi-random climate data. The comparison identifies a methodology to determine if a particular region on a correlation map may be explained by a physical mechanism or is simply statistical chance.

Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.

Science.gov (United States)

Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H

2017-07-01

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in

Spatial-temporal variability of leaf chlorophyll and its relationship with cocoa yield

Directory of Open Access Journals (Sweden)

Caique C. Medauar

Full Text Available ABSTRACT The objective of this study was to evaluate the spatial-temporal variability of leaf chlorophyll index and its relationship with cocoa yield. The experiment was carried out in an experimental area of cocoa production located in Ilhéus, Bahia State, Brazil. Leaf chlorophyll content was measured in September, October, January, February, March and April in the 2014/2015 season, at each sampling point of a regular grid by using a portable chlorophyll meter. Under the same conditions, yield was evaluated and the data were submitted to descriptive statistics and a linear correlation study. Geostatistical analysis was used to determine and quantify the spatial and temporal variability of leaf chlorophyll index and yield. Leaf chlorophyll index varied over the period evaluated, but the months of February, March and April showed no spatial dependence in the study area, indicating absence of temporal stability. Cocoa monthly yield, except in January, presented high spatial variability. Under the conditions of this study, it was not possible to establish a relationship between leaf chlorophyll index and cocoa yield.

Directional semivariogram analysis to identify and rank controls on the spatial variability of fracture networks

Science.gov (United States)

Hanke, John R.; Fischer, Mark P.; Pollyea, Ryan M.

2018-03-01

In this study, the directional semivariogram is deployed to investigate the spatial variability of map-scale fracture network attributes in the Paradox Basin, Utah. The relative variability ratio (R) is introduced as the ratio of integrated anisotropic semivariogram models, and R is shown to be an effective metric for quantifying the magnitude of spatial variability for any two azimuthal directions. R is applied to a GIS-based data set comprising roughly 1200 fractures, in an area which is bounded by a map-scale anticline and a km-scale normal fault. This analysis reveals that proximity to the fault strongly influences the magnitude of spatial variability for both fracture intensity and intersection density within 1-2 km. Additionally, there is significant anisotropy in the spatial variability, which is correlated with trends of the anticline and fault. The direction of minimum spatial correlation is normal to the fault at proximal distances, and gradually rotates and becomes subparallel to the fold axis over the same 1-2 km distance away from the fault. We interpret these changes to reflect varying scales of influence of the fault and the fold on fracture network development: the fault locally influences the magnitude and variability of fracture network attributes, whereas the fold sets the background level and structure of directional variability.

Spatial and temporal variability of winds in the Northern European Seas

DEFF Research Database (Denmark)

Karagali, Ioanna; Badger, Merete; Hahmann, Andrea N.

2013-01-01

the spatial and temporal variability of the near-surface wind field, including the inter- and intra-annual variability for resource assessment purposes. This study demonstrates the applicability of satellite observations as the means to provide information useful for selecting areas to perform higher...

The trade-off between spatial and temporal variabilities in reciprocal upper-limb aiming movements of different durations.

Directory of Open Access Journals (Sweden)

Frederic Danion

Full Text Available The spatial and temporal aspects of movement variability have typically been studied separately. As a result the relationship between spatial and temporal variabilities remains largely unknown. In two experiments we examined the evolution and covariation of spatial and temporal variabilities over variations in the duration of reciprocal aiming movements. Experiments differed in settings: In Experiment 1 participants moved unperturbed whereas in Experiment 2 they were confronted with an elastic force field. Different movement durations-for a constant inter-target distance-were either evoked by imposing spatial accuracy constraints while requiring participants to move as fast as possible, or prescribed by means of an auditory metronome while requiring participants to maximize spatial accuracy. Analyses focused on absolute and relative variabilities, respectively captured by the standard deviation (SD and the coefficient of variation (CV = SD/mean. Spatial variability (both SDspace and CVspace decreased with movement duration, while temporal variability (both SDtime and CVtime increased with movement duration. We found strong negative correlations between spatial and temporal variabilities over variations in movement duration, whether the variability examined was absolute or relative. These findings observed at the level of the full movement contrasted with the findings observed at the level of the separate acceleration and deceleration phases of movement. During the separate acceleration and deceleration phases both spatial and temporal variabilities (SD and CV were found to increase with their respective durations, leading to positive correlations between them. Moreover, variability was generally larger at the level of the constituent movement phases than at the level of the full movement. The general pattern of results was robust, as it emerged in both tasks in each of the two experiments. We conclude that feedback mechanisms operating to

Raw and Central Moments of Binomial Random Variables via Stirling Numbers

Science.gov (United States)

Griffiths, Martin

2013-01-01

We consider here the problem of calculating the moments of binomial random variables. It is shown how formulae for both the raw and the central moments of such random variables may be obtained in a recursive manner utilizing Stirling numbers of the first kind. Suggestions are also provided as to how students might be encouraged to explore this…

A review of instrumental variable estimators for Mendelian randomization.

Science.gov (United States)

Burgess, Stephen; Small, Dylan S; Thompson, Simon G

2017-10-01

Instrumental variable analysis is an approach for obtaining causal inferences on the effect of an exposure (risk factor) on an outcome from observational data. It has gained in popularity over the past decade with the use of genetic variants as instrumental variables, known as Mendelian randomization. An instrumental variable is associated with the exposure, but not associated with any confounder of the exposure-outcome association, nor is there any causal pathway from the instrumental variable to the outcome other than via the exposure. Under the assumption that a single instrumental variable or a set of instrumental variables for the exposure is available, the causal effect of the exposure on the outcome can be estimated. There are several methods available for instrumental variable estimation; we consider the ratio method, two-stage methods, likelihood-based methods, and semi-parametric methods. Techniques for obtaining statistical inferences and confidence intervals are presented. The statistical properties of estimates from these methods are compared, and practical advice is given about choosing a suitable analysis method. In particular, bias and coverage properties of estimators are considered, especially with weak instruments. Settings particularly relevant to Mendelian randomization are prioritized in the paper, notably the scenario of a continuous exposure and a continuous or binary outcome.

Spatial and temporal variability of interhemispheric transport times

Science.gov (United States)

Wu, Xiaokang; Yang, Huang; Waugh, Darryn W.; Orbe, Clara; Tilmes, Simone; Lamarque, Jean-Francois

2018-05-01

The seasonal and interannual variability of transport times from the northern midlatitude surface into the Southern Hemisphere is examined using simulations of three idealized age tracers: an ideal age tracer that yields the mean transit time from northern midlatitudes and two tracers with uniform 50- and 5-day decay. For all tracers the largest seasonal and interannual variability occurs near the surface within the tropics and is generally closely coupled to movement of the Intertropical Convergence Zone (ITCZ). There are, however, notable differences in variability between the different tracers. The largest seasonal and interannual variability in the mean age is generally confined to latitudes spanning the ITCZ, with very weak variability in the southern extratropics. In contrast, for tracers subject to spatially uniform exponential loss the peak variability tends to be south of the ITCZ, and there is a smaller contrast between tropical and extratropical variability. These differences in variability occur because the distribution of transit times from northern midlatitudes is very broad and tracers with more rapid loss are more sensitive to changes in fast transit times than the mean age tracer. These simulations suggest that the seasonal-interannual variability in the southern extratropics of trace gases with predominantly NH midlatitude sources may differ depending on the gases' chemical lifetimes.

Spatial Analysis of “Crazy Quilts”, a Class of Potentially Random Aesthetic Artefacts

Science.gov (United States)

Westphal-Fitch, Gesche; Fitch, W. Tecumseh

2013-01-01

Human artefacts in general are highly structured and often display ordering principles such as translational, reflectional or rotational symmetry. In contrast, human artefacts that are intended to appear random and non symmetrical are very rare. Furthermore, many studies show that humans find it extremely difficult to recognize or reproduce truly random patterns or sequences. Here, we attempt to model two-dimensional decorative spatial patterns produced by humans that show no obvious order. “Crazy quilts” represent a historically important style of quilt making that became popular in the 1870s, and lasted about 50 years. Crazy quilts are unusual because unlike most human artefacts, they are specifically intended to appear haphazard and unstructured. We evaluate the degree to which this intention was achieved by using statistical techniques of spatial point pattern analysis to compare crazy quilts with regular quilts from the same region and era and to evaluate the fit of various random distributions to these two quilt classes. We found that the two quilt categories exhibit fundamentally different spatial characteristics: The patch areas of crazy quilts derive from a continuous random distribution, while area distributions of regular quilts consist of Gaussian mixtures. These Gaussian mixtures derive from regular pattern motifs that are repeated and we suggest that such a mixture is a distinctive signature of human-made visual patterns. In contrast, the distribution found in crazy quilts is shared with many other naturally occurring spatial patterns. Centroids of patches in the two quilt classes are spaced differently and in general, crazy quilts but not regular quilts are well-fitted by a random Strauss process. These results indicate that, within the constraints of the quilt format, Victorian quilters indeed achieved their goal of generating random structures. PMID:24066095

Snowpack spatial and temporal variability assessment using SMP high-resolution penetrometer

Science.gov (United States)

Komarov, Anton; Seliverstov, Yuriy; Sokratov, Sergey; Grebennikov, Pavel

2017-04-01

This research is focused on study of spatial and temporal variability of structure and characteristics of snowpack, quick identification of layers based on hardness and dispersion values received from snow micro penetrometer (SMP). We also discuss the detection of weak layers and definition of their parameters in non-alpine terrain. As long as it is the first SMP tool available in Russia, our intent is to test it in different climate and weather conditions. During two separate snowpack studies in plain and mountain landscapes, we derived density and grain size profiles by comparing snow density and grain size from snowpits and SMP measurements. The first case study was MSU meteorological observatory test site in Moscow. SMP data was obtained by 6 consecutive measurements along 10 m transects with a horizontal resolution of approximately 50 cm. The detailed description of snowpack structure, density, grain size, air and snow temperature was also performed. By comparing this information, the detailed scheme of snowpack evolution was created. The second case study was in Khibiny mountains. One 10-meter-long transect was made. SMP, density, grain size and snow temperature data was obtained with horizontal resolution of approximately 50 cm. The high-definition profile of snowpack density variation was acquired using received data. The analysis of data reveals high spatial and temporal variability in snow density and layer structure in both horizontal and vertical dimensions. It indicates that the spatial variability is exhibiting similar spatial patterns as surface topology. This suggests a strong influence from such factors as wind and liquid water pressure on the temporal and spatial evolution of snow structure. It was also defined, that spatial variation of snowpack characteristics is substantial even within homogeneous plain landscape, while in high-latitude mountain regions it grows significantly.

Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness

Science.gov (United States)

Li, Jin; Tran, Maggie; Siwabessy, Justy

2016-01-01

Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and

Influence of bladder and rectal volume on spatial variability of a bladder tumor during radical radiotherapy

International Nuclear Information System (INIS)

Pos, Floris J.; Koedooder, Kees; Hulshof, Maarten C.C.M.; Tienhoven, Geertjan van; Gonzalez Gonzalez, Dionisio

2003-01-01

Purpose: To assess the spatial variability of a bladder tumor relative to the planning target volume boundaries during radical radiotherapy, and furthermore to develop strategies to reduce spatial variability. Methods and Materials: Seventeen patients with solitary T2-T4N0M0 bladder cancer were treated with a technique delivering 40 Gy/2 Gy in 20 fractions to the whole bladder with a concomitant boost to the bladder tumor of 20 Gy in 1 Gy fractions in an overall time of 4 weeks. CT scans were made weekly, immediately after treatment, and matched with the planning CT scan. Spatial variability of the tumor, as well as bladder volume and rectal diameter, were scored for each patient each week. Results: In 65% of patients, a part of the tumor appeared outside the planning target volume boundaries at least one time during the course of radiotherapy. No consistent relation of this variability with time was found. Bladder volumes and rectal diameters showed marked variability during the course of treatment. A large initial bladder volume and rectal diameter predicted a large volume variation and a large tumor spatial variability. Conclusion: In this study, a margin of 1.5 to 2 cm seemed to be inadequate in 65% of the patients with respect to spatial variability. Bladder volume and rectal diameter were found to be predictive for spatial variability of a bladder tumor during concomitant boost radiotherapy

Influence of bladder and rectal volume on spatial variability of a bladder tumor during radical radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Pos, Floris J; Koedooder, Kees; Hulshof, Maarten C.C.M.; Tienhoven, Geertjan van; Gonzalez Gonzalez, Dionisio

2003-03-01

Purpose: To assess the spatial variability of a bladder tumor relative to the planning target volume boundaries during radical radiotherapy, and furthermore to develop strategies to reduce spatial variability. Methods and Materials: Seventeen patients with solitary T2-T4N0M0 bladder cancer were treated with a technique delivering 40 Gy/2 Gy in 20 fractions to the whole bladder with a concomitant boost to the bladder tumor of 20 Gy in 1 Gy fractions in an overall time of 4 weeks. CT scans were made weekly, immediately after treatment, and matched with the planning CT scan. Spatial variability of the tumor, as well as bladder volume and rectal diameter, were scored for each patient each week. Results: In 65% of patients, a part of the tumor appeared outside the planning target volume boundaries at least one time during the course of radiotherapy. No consistent relation of this variability with time was found. Bladder volumes and rectal diameters showed marked variability during the course of treatment. A large initial bladder volume and rectal diameter predicted a large volume variation and a large tumor spatial variability. Conclusion: In this study, a margin of 1.5 to 2 cm seemed to be inadequate in 65% of the patients with respect to spatial variability. Bladder volume and rectal diameter were found to be predictive for spatial variability of a bladder tumor during concomitant boost radiotherapy.

A Particle Swarm Optimization Algorithm with Variable Random Functions and Mutation

Institute of Scientific and Technical Information of China (English)

ZHOU Xiao-Jun; YANG Chun-Hua; GUI Wei-Hua; DONG Tian-Xue

2014-01-01

The convergence analysis of the standard particle swarm optimization (PSO) has shown that the changing of random functions, personal best and group best has the potential to improve the performance of the PSO. In this paper, a novel strategy with variable random functions and polynomial mutation is introduced into the PSO, which is called particle swarm optimization algorithm with variable random functions and mutation (PSO-RM). Random functions are adjusted with the density of the population so as to manipulate the weight of cognition part and social part. Mutation is executed on both personal best particle and group best particle to explore new areas. Experiment results have demonstrated the effectiveness of the strategy.

The Spatial Variability of Beryllium-7 Depth Distribution Study

International Nuclear Information System (INIS)

Jalal Sharib; Zainudin Othman; Dainee Nor Fardzila Ahmad Tugi; Noor Fadzilah Yusof; Mohd Tarmizi Ishak

2015-01-01

The objective of this paper is to study the spatial variability of 7 Be depth evolution in soil profile at two different sampling sites. The soil samples have been collected by using metal core in bare area in Bangi, Selangor and Timah Tasoh, Perlis , Malaysia. Two composite core samples for each sampling sites has been sectioned into 2 mm increments to a depth of 4 cm and oven dried at 45- 60 degree Celsius and gently desegregated. These two composite spatial samples are passed through a < 2 mm sieve and packed into proper geometry plastic container for 7 Be analysis by using gamma spectrometry with a 24-hour count time. From the findings, the 7 Be content in the soil samples from Bangi, Selangor study area is distributed lower depth penetration into the soil profile than Timah Tasoh, Perlis catchment due to many factors such as precipitation (fallout) and others. However, the spatial variability from both samples study area is also decreases exponentially with depth and is confined within the top few centimeters and similar with other works been reported (Blake et al., (2000) and Walling et al.,(2008). Furthermore, a detailed discussion from this study findings will be in full papers. (author)

Spatial Variability of Soil Morphorlogical and Physico-Chemical ...

African Journals Online (AJOL)

Spatial Variability of Soil Morphorlogical and Physico-Chemical Properties in Ladoke Akintola University of Technology Cashew Plantation, Ogbomoso. ... Colour (AP, B1 B2 and B3), structure (B2 and B3), stoniness (B1, B2 and B3), concretion (AP B1, B2 and B3) and boundary forms (B1, B2 and B3) have extremely ...

Spatial variability in branchial basket meristics and morphology of ...

African Journals Online (AJOL)

We examined spatial variability in meristic and morphological characteristics of the branchial basket of sardine Sardinops sagax collected from four geographical regions around the southern African coast, namely Namibia and the South African west, south and east coasts. Our analysis tested the hypothesis of three putative ...

On mean square displacement behaviors of anomalous diffusions with variable and random orders

International Nuclear Information System (INIS)

Sun Hongguang; Chen Wen; Sheng Hu; Chen Yangquan

2010-01-01

Mean square displacement (MSD) is used to characterize anomalous diffusion. Recently, models of anomalous diffusion with variable-order and random-order were proposed, but no MSD analysis has been given so far. The purpose of this Letter is to offer a concise derivation of MSD functions for the variable-order model and the random-order model. Numerical results are presented to illustrate the analytical results. In addition, we show how to establish a variable-random-order model for a given MSD function which has clear application potentials.

Tannat grape composition responses to spatial variability of temperature in an Uruguay's coastal wine region

Science.gov (United States)

Fourment, Mercedes; Ferrer, Milka; González-Neves, Gustavo; Barbeau, Gérard; Bonnardot, Valérie; Quénol, Hervé

2017-09-01

Spatial variability of temperature was studied in relation to the berry basic composition and secondary compounds of the Tannat cultivar at harvest from vineyards located in Canelones and Montevideo, the most important wine region of Uruguay. Monitoring of berries and recording of temperature were performed in 10 commercial vineyards of Tannat situated in the southern coastal wine region of the country for three vintages (2012, 2013, and 2014). Results from a multivariate correlation analysis between berry composition and temperature over the three vintages showed that (1) Tannat responses to spatial variability of temperature were different over the vintages, (2) correlations between secondary metabolites and temperature were higher than those between primary metabolites, and (3) correlation values between berry composition and climate variables increased when ripening occurred under dry conditions (below average rainfall). For a particular studied vintage (2013), temperatures explained 82.5% of the spatial variability of the berry composition. Daily thermal amplitude was found to be the most important spatial mode of variability with lower values recorded at plots nearest to the sea and more exposed to La Plata River. The highest levels in secondary compounds were found in berries issued from plots situated as far as 18.3 km from La Plata River. The increasing knowledge of temperature spatial variability and its impact on grape berry composition contributes to providing possible issues to adapt grapevine to climate change.

Quantifying measurement uncertainty and spatial variability in the context of model evaluation

Science.gov (United States)

Choukulkar, A.; Brewer, A.; Pichugina, Y. L.; Bonin, T.; Banta, R. M.; Sandberg, S.; Weickmann, A. M.; Djalalova, I.; McCaffrey, K.; Bianco, L.; Wilczak, J. M.; Newman, J. F.; Draxl, C.; Lundquist, J. K.; Wharton, S.; Olson, J.; Kenyon, J.; Marquis, M.

2017-12-01

In an effort to improve wind forecasts for the wind energy sector, the Department of Energy and the NOAA funded the second Wind Forecast Improvement Project (WFIP2). As part of the WFIP2 field campaign, a large suite of in-situ and remote sensing instrumentation was deployed to the Columbia River Gorge in Oregon and Washington from October 2015 - March 2017. The array of instrumentation deployed included 915-MHz wind profiling radars, sodars, wind- profiling lidars, and scanning lidars. The role of these instruments was to provide wind measurements at high spatial and temporal resolution for model evaluation and improvement of model physics. To properly determine model errors, the uncertainties in instrument-model comparisons need to be quantified accurately. These uncertainties arise from several factors such as measurement uncertainty, spatial variability, and interpolation of model output to instrument locations, to name a few. In this presentation, we will introduce a formalism to quantify measurement uncertainty and spatial variability. The accuracy of this formalism will be tested using existing datasets such as the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign. Finally, the uncertainties in wind measurement and the spatial variability estimates from the WFIP2 field campaign will be discussed to understand the challenges involved in model evaluation.

Rainfall interception and spatial variability of throughfall in spruce stand

Directory of Open Access Journals (Sweden)

Dohnal Michal

2014-12-01

Full Text Available The interception was recognized as an important part of the catchment water balance in temperate climate. The mountainous forest ecosystem at experimental headwater catchment Liz has been subject of long-term monitoring. Unique dataset in terms of time resolution serves to determine canopy storage capacity and free throughfall. Spatial variability of throughfall was studied using one weighing and five tipping bucket rain gauges. The basic characteristics of forest affecting interception process were determined for the Norway spruce stand at the experimental area - the leaf area index was 5.66 - 6.00 m2 m-2, the basal area was 55.7 m2 ha-1, and the crown closure above individual rain gauges was between 19 and 95%. The total interception loss in both growing seasons analyzed was 34.5%. The mean value of the interception capacity determined was about 2 mm. Throughfall exhibited high variability from place to place and it was strongly affected by character of rainfall. On the other hand, spatial pattern of throughfall in average showed low variability.

Spatial variability of chemical properties of soil under pasture

Directory of Open Access Journals (Sweden)

Samuel Ferreira da Silva

2016-04-01

Full Text Available The objective of this study was to analyze the spatial variability of soil chemical attributes under pasture, as well as lime and fertilizer recommendations based on the interpretation of soil chemical analysis from two sampling methods: conventional and systematic depths of 0 to 10 and 10 to 20 cm. The study was conducted at IFES-campus Alegre-ES. Data analysis was performed using descriptive statistics and geostatistics. Results indicate that the spatial method enabled the identification of deficit areas and excessive liming and fertilization, which could not be defined by the conventional method.

Spatial variability of correlated color temperature of lightning channels

Directory of Open Access Journals (Sweden)

Nobuaki Shimoji

Full Text Available In this paper, we present the spatial variability of the correlated color temperature of lightning channel shown in a digital still image. In order to analyze the correlated color temperature, we calculated chromaticity coordinates of the lightning channels in the digital still image. From results, the spatial variation of the correlated color temperature of the lightning channel was confirmed. Moreover, the results suggest that the correlated color temperature and peak current of the lightning channels are related to each other. Keywords: Lightning, Color analysis, Correlated color temperature, Chromaticity coordinate, CIE 1931 xy-chromaticity diagram

Limit theorems for multi-indexed sums of random variables

CERN Document Server

Klesov, Oleg

2014-01-01

Presenting the first unified treatment of limit theorems for multiple sums of independent random variables, this volume fills an important gap in the field. Several new results are introduced, even in the classical setting, as well as some new approaches that are simpler than those already established in the literature. In particular, new proofs of the strong law of large numbers and the Hajek-Renyi inequality are detailed. Applications of the described theory include Gibbs fields, spin glasses, polymer models, image analysis and random shapes. Limit theorems form the backbone of probability theory and statistical theory alike. The theory of multiple sums of random variables is a direct generalization of the classical study of limit theorems, whose importance and wide application in science is unquestionable. However, to date, the subject of multiple sums has only been treated in journals. The results described in this book will be of interest to advanced undergraduates, graduate students and researchers who ...

Compound Poisson Approximations for Sums of Random Variables

OpenAIRE

Serfozo, Richard F.

1986-01-01

We show that a sum of dependent random variables is approximately compound Poisson when the variables are rarely nonzero and, given they are nonzero, their conditional distributions are nearly identical. We give several upper bounds on the total-variation distance between the distribution of such a sum and a compound Poisson distribution. Included is an example for Markovian occurrences of a rare event. Our bounds are consistent with those that are known for Poisson approximations for sums of...

Deciphering factors controlling groundwater arsenic spatial variability in Bangladesh

Science.gov (United States)

Tan, Z.; Yang, Q.; Zheng, C.; Zheng, Y.

2017-12-01

Elevated concentrations of geogenic arsenic in groundwater have been found in many countries to exceed 10 μg/L, the WHO's guideline value for drinking water. A common yet unexplained characteristic of groundwater arsenic spatial distribution is the extensive variability at various spatial scales. This study investigates factors influencing the spatial variability of groundwater arsenic in Bangladesh to improve the accuracy of models predicting arsenic exceedance rate spatially. A novel boosted regression tree method is used to establish a weak-learning ensemble model, which is compared to a linear model using a conventional stepwise logistic regression method. The boosted regression tree models offer the advantage of parametric interaction when big datasets are analyzed in comparison to the logistic regression. The point data set (n=3,538) of groundwater hydrochemistry with 19 parameters was obtained by the British Geological Survey in 2001. The spatial data sets of geological parameters (n=13) were from the Consortium for Spatial Information, Technical University of Denmark, University of East Anglia and the FAO, while the soil parameters (n=42) were from the Harmonized World Soil Database. The aforementioned parameters were regressed to categorical groundwater arsenic concentrations below or above three thresholds: 5 μg/L, 10 μg/L and 50 μg/L to identify respective controlling factors. Boosted regression tree method outperformed logistic regression methods in all three threshold levels in terms of accuracy, specificity and sensitivity, resulting in an improvement of spatial distribution map of probability of groundwater arsenic exceeding all three thresholds when compared to disjunctive-kriging interpolated spatial arsenic map using the same groundwater arsenic dataset. Boosted regression tree models also show that the most important controlling factors of groundwater arsenic distribution include groundwater iron content and well depth for all three

Modeling the spatial distribution of Chagas disease vectors using environmental variables and people´s knowledge.

Science.gov (United States)

Hernández, Jaime; Núñez, Ignacia; Bacigalupo, Antonella; Cattan, Pedro E

2013-05-31

Chagas disease is caused by the protozoan Trypanosoma cruzi, which is transmitted to mammal hosts by triatomine insect vectors. The goal of this study was to model the spatial distribution of triatomine species in an endemic area. Vector's locations were obtained with a rural householders' survey. This information was combined with environmental data obtained from remote sensors, land use maps and topographic SRTM data, using the machine learning algorithm Random Forests to model species distribution. We analysed the combination of variables on three scales: 10 km, 5 km and 2.5 km cell size grids. The best estimation, explaining 46.2% of the triatomines spatial distribution, was obtained for 5 km of spatial resolution. Presence probability distribution increases from central Chile towards the north, tending to cover the central-coastal region and avoiding areas of the Andes range. The methodology presented here was useful to model the distribution of triatomines in an endemic area; it is best explained using 5 km of spatial resolution, and their presence increases in the northern part of the study area. This study's methodology can be replicated in other countries with Chagas disease or other vectorial transmitted diseases, and be used to locate high risk areas and to optimize resource allocation, for prevention and control of vectorial diseases.

Spatial birth-and-death processes in random environment

OpenAIRE

Fernandez, Roberto; Ferrari, Pablo A.; Guerberoff, Gustavo R.

2004-01-01

We consider birth-and-death processes of objects (animals) defined in ${\\bf Z}^d$ having unit death rates and random birth rates. For animals with uniformly bounded diameter we establish conditions on the rate distribution under which the following holds for almost all realizations of the birth rates: (i) the process is ergodic with at worst power-law time mixing; (ii) the unique invariant measure has exponential decay of (spatial) correlations; (iii) there exists a perfect-simulation algorit...

Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas - a review

Science.gov (United States)

Cristiano, Elena; ten Veldhuis, Marie-claire; van de Giesen, Nick

2017-07-01

In urban areas, hydrological processes are characterized by high variability in space and time, making them sensitive to small-scale temporal and spatial rainfall variability. In the last decades new instruments, techniques, and methods have been developed to capture rainfall and hydrological processes at high resolution. Weather radars have been introduced to estimate high spatial and temporal rainfall variability. At the same time, new models have been proposed to reproduce hydrological response, based on small-scale representation of urban catchment spatial variability. Despite these efforts, interactions between rainfall variability, catchment heterogeneity, and hydrological response remain poorly understood. This paper presents a review of our current understanding of hydrological processes in urban environments as reported in the literature, focusing on their spatial and temporal variability aspects. We review recent findings on the effects of rainfall variability on hydrological response and identify gaps where knowledge needs to be further developed to improve our understanding of and capability to predict urban hydrological response.

Generalized index for spatial data sets as a measure of complete spatial randomness

Science.gov (United States)

Hackett-Jones, Emily J.; Davies, Kale J.; Binder, Benjamin J.; Landman, Kerry A.

2012-06-01

Spatial data sets, generated from a wide range of physical systems can be analyzed by counting the number of objects in a set of bins. Previous work has been limited to equal-sized bins, which are inappropriate for some domains (e.g., circular). We consider a nonequal size bin configuration whereby overlapping or nonoverlapping bins cover the domain. A generalized index, defined in terms of a variance between bin counts, is developed to indicate whether or not a spatial data set, generated from exclusion or nonexclusion processes, is at the complete spatial randomness (CSR) state. Limiting values of the index are determined. Using examples, we investigate trends in the generalized index as a function of density and compare the results with those using equal size bins. The smallest bin size must be much larger than the mean size of the objects. We can determine whether a spatial data set is at the CSR state or not by comparing the values of a generalized index for different bin configurations—the values will be approximately the same if the data is at the CSR state, while the values will differ if the data set is not at the CSR state. In general, the generalized index is lower than the limiting value of the index, since objects do not have access to the entire region due to blocking by other objects. These methods are applied to two applications: (i) spatial data sets generated from a cellular automata model of cell aggregation in the enteric nervous system and (ii) a known plant data distribution.

Quantifying spatial distribution of snow depth errors from LiDAR using Random Forests

Science.gov (United States)

Tinkham, W.; Smith, A. M.; Marshall, H.; Link, T. E.; Falkowski, M. J.; Winstral, A. H.

2013-12-01

There is increasing need to characterize the distribution of snow in complex terrain using remote sensing approaches, especially in isolated mountainous regions that are often water-limited, the principal source of terrestrial freshwater, and sensitive to climatic shifts and variations. We apply intensive topographic surveys, multi-temporal LiDAR, and Random Forest modeling to quantify snow volume and characterize associated errors across seven land cover types in a semi-arid mountainous catchment at a 1 and 4 m spatial resolution. The LiDAR-based estimates of both snow-off surface topology and snow depths were validated against ground-based measurements across the catchment. Comparison of LiDAR-derived snow depths to manual snow depth surveys revealed that LiDAR based estimates were more accurate in areas of low lying vegetation such as shrubs (RMSE = 0.14 m) as compared to areas consisting of tree cover (RMSE = 0.20-0.35 m). The highest errors were found along the edge of conifer forests (RMSE = 0.35 m), however a second conifer transect outside the catchment had much lower errors (RMSE = 0.21 m). This difference is attributed to the wind exposure of the first site that led to highly variable snow depths at short spatial distances. The Random Forest modeled errors deviated from the field measured errors with a RMSE of 0.09-0.34 m across the different cover types. Results show that snow drifts, which are important for maintaining spring and summer stream flows and establishing and sustaining water-limited plant species, contained 30 × 5-6% of the snow volume while only occupying 10% of the catchment area similar to findings by prior physically-based modeling approaches. This study demonstrates the potential utility of combining multi-temporal LiDAR with Random Forest modeling to quantify the distribution of snow depth with a reasonable degree of accuracy. Future work could explore the utility of Terrestrial LiDAR Scanners to produce validation of snow-on surface

Comparing spatial regression to random forests for large environmental data sets

Science.gov (United States)

Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputatio...

Randomized trial of intermittent or continuous amnioinfusion for variable decelerations.

Science.gov (United States)

Rinehart, B K; Terrone, D A; Barrow, J H; Isler, C M; Barrilleaux, P S; Roberts, W E

2000-10-01

To determine whether continuous or intermittent bolus amnioinfusion is more effective in relieving variable decelerations. Patients with repetitive variable decelerations were randomized to an intermittent bolus or continuous amnioinfusion. The intermittent bolus infusion group received boluses of 500 mL of normal saline, each over 30 minutes, with boluses repeated if variable decelerations recurred. The continuous infusion group received a bolus infusion of 500 mL of normal saline over 30 minutes and then 3 mL per minute until delivery occurred. The ability of the amnioinfusion to abolish variable decelerations was analyzed, as were maternal demographic and pregnancy outcome variables. Power analysis indicated that 64 patients would be required. Thirty-five patients were randomized to intermittent infusion and 30 to continuous infusion. There were no differences between groups in terms of maternal demographics, gestational age, delivery mode, neonatal outcome, median time to resolution of variable decelerations, or the number of times variable decelerations recurred. The median volume infused in the intermittent infusion group (500 mL) was significantly less than that in the continuous infusion group (905 mL, P =.003). Intermittent bolus amnioinfusion is as effective as continuous infusion in relieving variable decelerations in labor. Further investigation is necessary to determine whether either of these techniques is associated with increased occurrence of rare complications such as cord prolapse or uterine rupture.

Randomized Symmetric Crypto Spatial Fusion Steganographic System

Directory of Open Access Journals (Sweden)

Viswanathan Perumal

2016-06-01

Full Text Available The image fusion steganographic system embeds encrypted messages in decomposed multimedia carriers using a pseudorandom generator but it fails to evaluate the contents of the cover image. This results in the secret data being embedded in smooth regions, which leads to visible distortion that affects the imperceptibility and confidentiality. To solve this issue, as well as to improve the quality and robustness of the system, the Randomized Symmetric Crypto Spatial Fusion Steganography System is proposed in this study. It comprises three-subsystem bitwise encryption, spatial fusion, and bitwise embedding. First, bitwise encryption encrypts the message using bitwise operation to improve the confidentiality. Then, spatial fusion decomposes and evaluates the region of embedding on the basis of sharp intensity and capacity. This restricts the visibility of distortion and provides a high embedding capacity. Finally, the bitwise embedding system embeds the encrypted message through differencing the pixels in the region by 1, checking even or odd options and not equal to zero constraints. This reduces the modification rate to avoid distortion. The proposed heuristic algorithm is implemented in the blue channel, to which the human visual system is less sensitive. It was tested using standard IST natural images with steganalysis algorithms and resulted in better quality, imperceptibility, embedding capacity and invulnerability to various attacks compared to other steganographic systems.

ESEARCH OF THE LAW OF DISTRIBUTION OF THE RANDOM VARIABLE OF THE COMPRESSION

Directory of Open Access Journals (Sweden)

I. Sarayeva

2011-01-01

Full Text Available At research of diagnosing the process of modern automobile engines by means of methods of mathematical statistics the experimental data of the random variable of compression are analysed and it is proved that the random variable of compression has the form of the normal law of distribution.

Measured spatial variability of beach erosion due to aeolian processes.

NARCIS (Netherlands)

de Vries, S.; Verheijen, A.H.; Hoonhout, B.M.; Vos, S.E.; Cohn, Nicholas; Ruggiero, P; Aagaard, T.; Deigaard, R.; Fuhrman, D.

2017-01-01

This paper shows the first results of measured spatial variability of beach erosion due to aeolian processes during the recently conducted SEDEX2 field experiment at Long Beach, Washington, U.S.A.. Beach erosion and sedimentation were derived using series of detailed terrestrial LIDAR measurements

The spatial distribution of known predictors of autism spectrum disorders impacts geographic variability in prevalence in central North Carolina

Directory of Open Access Journals (Sweden)

Hoffman Kate

2012-10-01

Full Text Available Abstract Background The causes of autism spectrum disorders (ASD remain largely unknown and widely debated; however, evidence increasingly points to the importance of environmental exposures. A growing number of studies use geographic variability in ASD prevalence or exposure patterns to investigate the association between environmental factors and ASD. However, differences in the geographic distribution of established risk and predictive factors for ASD, such as maternal education or age, can interfere with investigations of ASD etiology. We evaluated geographic variability in the prevalence of ASD in central North Carolina and the impact of spatial confounding by known risk and predictive factors. Methods Children meeting a standardized case definition for ASD at 8 years of age were identified through records-based surveillance for 8 counties biennially from 2002 to 2008 (n=532. Vital records were used to identify the underlying cohort (15% random sample of children born in the same years as children with an ASD, n=11,034, and to obtain birth addresses. We used generalized additive models (GAMs to estimate the prevalence of ASD across the region by smoothing latitude and longitude. GAMs, unlike methods used in previous spatial analyses of ASD, allow for extensive adjustment of individual-level risk factors (e.g. maternal age and education when evaluating spatial variability of disease prevalence. Results Unadjusted maps revealed geographic variation in surveillance-recognized ASD. Children born in certain regions of the study area were up to 1.27 times as likely to be recognized as having ASD compared to children born in the study area as a whole (prevalence ratio (PR range across the study area 0.57-1.27; global P=0.003. However, geographic gradients of ASD prevalence were attenuated after adjusting for spatial confounders (adjusted PR range 0.72-1.12 across the study area; global P=0.052. Conclusions In these data, spatial variation of ASD

Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review

Directory of Open Access Journals (Sweden)

E. Cristiano

2017-07-01

Full Text Available In urban areas, hydrological processes are characterized by high variability in space and time, making them sensitive to small-scale temporal and spatial rainfall variability. In the last decades new instruments, techniques, and methods have been developed to capture rainfall and hydrological processes at high resolution. Weather radars have been introduced to estimate high spatial and temporal rainfall variability. At the same time, new models have been proposed to reproduce hydrological response, based on small-scale representation of urban catchment spatial variability. Despite these efforts, interactions between rainfall variability, catchment heterogeneity, and hydrological response remain poorly understood. This paper presents a review of our current understanding of hydrological processes in urban environments as reported in the literature, focusing on their spatial and temporal variability aspects. We review recent findings on the effects of rainfall variability on hydrological response and identify gaps where knowledge needs to be further developed to improve our understanding of and capability to predict urban hydrological response.

In-Situ Spatial Variability Of Thermal Conductivity And Volumetric ...

African Journals Online (AJOL)

Studies of spatial variability of thermal conductivity and volumetric water content of silty topsoil were conduct-ed on a 0.6 ha site at Abeokuta, South-Western Nigeria. The thermal conductivity (k) was measured at depths of up to 0.06 m along four parallel profiles of 200 m long and at an average temperature of 25 C, using ...

Spatial and temporal variability of hyperspectral signatures of terrain

Science.gov (United States)

Jones, K. F.; Perovich, D. K.; Koenig, G. G.

2008-04-01

Electromagnetic signatures of terrain exhibit significant spatial heterogeneity on a range of scales as well as considerable temporal variability. A statistical characterization of the spatial heterogeneity and spatial scaling algorithms of terrain electromagnetic signatures are required to extrapolate measurements to larger scales. Basic terrain elements including bare soil, grass, deciduous, and coniferous trees were studied in a quasi-laboratory setting using instrumented test sites in Hanover, NH and Yuma, AZ. Observations were made using a visible and near infrared spectroradiometer (350 - 2500 nm) and hyperspectral camera (400 - 1100 nm). Results are reported illustrating: i) several difference scenes; ii) a terrain scene time series sampled over an annual cycle; and iii) the detection of artifacts in scenes. A principal component analysis indicated that the first three principal components typically explained between 90 and 99% of the variance of the 30 to 40-channel hyperspectral images. Higher order principal components of hyperspectral images are useful for detecting artifacts in scenes.

Spatial variability of detrended soil plow layer penetrometer resistance transect in a sugarcane field

Science.gov (United States)

Pérez, Luis D.; Cumbrera, Ramiro; Mato, Juan; Millán, Humberto; Tarquis, Ana M.

2015-04-01

Spatial variability of soil properties is relevant for identifying those zones with physical degradation. In this sense, one has to face the problem of identifying the origin and distribution of spatial variability patterns (Brouder et al., 2001; Millán et al., 2012). The objective of the present work was to quantify the spatial structure of soil penetrometer resistance (PR) collected from a transect data consisted of 221 points equidistant. In each sampling, readings were obtained from 0 cm till 70 cm of depth, with an interval of 5 cm (Pérez, 2012). The study was conducted on a Vertisol (Typic Hapludert) dedicated to sugarcane (Saccharum officinarum L.) production during the last sixty years (Pérez et al., 2010). Recently, scaling approach has been applied on the determination of the scaling data properties (Tarquis et al., 2008; Millán et al., 2012; Pérez, 2012). We focus in the Hurst analysis to characterize the data variability for each depth. Previously a detrended analysis was conducted in order to better study de intrinsic variability of the series. The Hurst exponent (H) for each depth was estimated showing a characteristic pattern and differentiating PR evolution in depth. References Brouder, S., Hofmann, B., Reetz, H.F., 2001. Evaluating spatial variability of soil parameters for input management. Better Crops 85, 8-11. Millán, H; AM Tarquís, Luís D. Pérez, Juan Mato, Mario González-Posada, 2012. Spatial variability patterns of some Vertisol properties at a field scale using standardized data. Soil and Tillage Research, 120, 76-84. Pérez, Luís D. 2012. Influencia de la maquinaria agrícola sobre la variabilidad espacial de la compactación del suelo. Aplicación de la metodología geoestadística-fractal. PhD thesis, UPM (In Spanish). Pérez, Luís D., Humberto Millán, Mario González-Posada 2010. Spatial complexity of soil plow layer penetrometer resistance as influenced by sugarcane harvesting: A prefractal approach. Soil and Tillage

Characteristics of quantum open systems: free random variables approach

International Nuclear Information System (INIS)

Gudowska-Nowak, E.; Papp, G.; Brickmann, J.

1998-01-01

Random Matrix Theory provides an interesting tool for modelling a number of phenomena where noises (fluctuations) play a prominent role. Various applications range from the theory of mesoscopic systems in nuclear and atomic physics to biophysical models, like Hopfield-type models of neural networks and protein folding. Random Matrix Theory is also used to study dissipative systems with broken time-reversal invariance providing a setup for analysis of dynamic processes in condensed, disordered media. In the paper we use the Random Matrix Theory (RMT) within the formalism of Free Random Variables (alias Blue's functions), which allows to characterize spectral properties of non-Hermitean ''Hamiltonians''. The relevance of using the Blue's function method is discussed in connection with application of non-Hermitean operators in various problems of physical chemistry. (author)

Spatial variability of excess mortality during prolonged dust events in a high-density city: a time-stratified spatial regression approach.

Science.gov (United States)

Wong, Man Sing; Ho, Hung Chak; Yang, Lin; Shi, Wenzhong; Yang, Jinxin; Chan, Ta-Chien

2017-07-24

Dust events have long been recognized to be associated with a higher mortality risk. However, no study has investigated how prolonged dust events affect the spatial variability of mortality across districts in a downwind city. In this study, we applied a spatial regression approach to estimate the district-level mortality during two extreme dust events in Hong Kong. We compared spatial and non-spatial models to evaluate the ability of each regression to estimate mortality. We also compared prolonged dust events with non-dust events to determine the influences of community factors on mortality across the city. The density of a built environment (estimated by the sky view factor) had positive association with excess mortality in each district, while socioeconomic deprivation contributed by lower income and lower education induced higher mortality impact in each territory planning unit during a prolonged dust event. Based on the model comparison, spatial error modelling with the 1st order of queen contiguity consistently outperformed other models. The high-risk areas with higher increase in mortality were located in an urban high-density environment with higher socioeconomic deprivation. Our model design shows the ability to predict spatial variability of mortality risk during an extreme weather event that is not able to be estimated based on traditional time-series analysis or ecological studies. Our spatial protocol can be used for public health surveillance, sustainable planning and disaster preparation when relevant data are available.

Mapping spatial variability of foliar nitrogen in coffee (Coffea arabica L.) plantations with multispectral Sentinel-2 MSI data

Science.gov (United States)

Chemura, Abel; Mutanga, Onisimo; Odindi, John; Kutywayo, Dumisani

2018-04-01

Nitrogen (N) is the most limiting factor to coffee development and productivity. Therefore, development of rapid, spatially explicit and temporal remote sensing-based approaches to determine spatial variability of coffee foliar N are imperative for increasing yields, reducing production costs and mitigating environmental impacts associated with excessive N applications. This study sought to assess the value of Sentinel-2 MSI spectral bands and vegetation indices in empirical estimation of coffee foliar N content at landscape level. Results showed that coffee foliar N is related to Sentinel-2 MSI B4 (R2 = 0.32), B6 (R2 = 0.49), B7 (R2 = 0.42), B8 (R2 = 0.57) and B12 (R2 = 0.24) bands. Vegetation indices were more related to coffee foliar N as shown by the Inverted Red-Edge Chlorophyll Index - IRECI (R2 = 0.66), Relative Normalized Difference Index - RNDVI (R2 = 0.48), CIRE1 (R2 = 0.28), and Normalized Difference Infrared Index - NDII (R2 = 0.37). These variables were also identified by the random forest variable optimisation as the most valuable in coffee foliar N prediction. Modelling coffee foliar N using vegetation indices produced better accuracy (R2 = 0.71 with RMSE = 0.27 for all and R2 = 0.73 with RMSE = 0.25 for optimized variables), compared to using spectral bands (R2 = 0.57 with RMSE = 0.32 for all and R2 = 0.58 with RMSE = 0.32 for optimized variables). Combining optimized bands and vegetation indices produced the best results in coffee foliar N modelling (R2 = 0.78, RMSE = 0.23). All the three best performing models (all vegetation indices, optimized vegetation indices and combining optimal bands and optimal vegetation indices) established that 15.2 ha (4.7%) of the total area under investigation had low foliar N levels (landscape scale.

Spatial and temporal variability of precipitation and drought in Portugal

Directory of Open Access Journals (Sweden)

D. S. Martins

2012-05-01

Full Text Available The spatial variability of precipitation and drought are investigated for Portugal using monthly precipitation from 74 stations and minimum and maximum temperature from 27 stations, covering the common period of 1941–2006. Seasonal precipitation and the corresponding percentages in the year, as well as the precipitation concentration index (PCI, was computed for all 74 stations and then used as an input matrix for an R-mode principal component analysis to identify the precipitation patterns. The standardized precipitation index at 3 and 12 month time scales were computed for all stations, whereas the Palmer Drought Severity Index (PDSI and the modified PDSI for Mediterranean conditions (MedPDSI were computed for the stations with temperature data. The spatial patterns of drought over Portugal were identified by applying the S-mode principal component analysis coupled with varimax rotation to the drought indices matrices. The result revealed two distinct sub-regions in the country relative to both precipitation regimes and drought variability. The analysis of time variability of the PC scores of all drought indices allowed verifying that there is no linear trend indicating drought aggravation or decrease. In addition, the analysis shows that results for SPI-3, SPI-12, PDSI and MedPDSI are coherent among them.

Groundwater Variability Across Temporal and Spatial Scales in the Central and Northeastern U.S.

Science.gov (United States)

Li, Bailing; Rodell, Matthew; Famiglietti, James S.

2015-01-01

Depth-to-water measurements from 181 monitoring wells in unconfined or semi-confined aquifers in nine regions of the central and northeastern U.S. were analyzed. Groundwater storage exhibited strong seasonal variations in all regions, with peaks in spring and lows in autumn, and its interannual variability was nearly unbounded, such that the impacts of droughts, floods, and excessive pumping could persist for many years. We found that the spatial variability of groundwater storage anomalies (deviations from the long term mean) increases as a power function of extent scale (square root of area). That relationship, which is linear on a log-log graph, is common to other hydrological variables but had never before been shown with groundwater data. We describe how the derived power function can be used to determine the number of wells needed to estimate regional mean groundwater storage anomalies with a desired level of accuracy, or to assess uncertainty in regional mean estimates from a set number of observations. We found that the spatial variability of groundwater storage anomalies within a region often increases with the absolute value of the regional mean anomaly, the opposite of the relationship between soil moisture spatial variability and mean. Recharge (drainage from the lowest model soil layer) simulated by the Variable Infiltration Capacity (VIC) model was compatible with observed monthly groundwater storage anomalies and month-to-month changes in groundwater storage.

Zero Distribution of System with Unknown Random Variables Case Study: Avoiding Collision Path

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Parman Setyamartana

2014-07-01

Full Text Available This paper presents the stochastic analysis of finding the feasible trajectories of robotics arm motion at obstacle surrounding. Unknown variables are coefficients of polynomials joint angle so that the collision-free motion is achieved. ãk is matrix consisting of these unknown feasible polynomial coefficients. The pattern of feasible polynomial in the obstacle environment shows as random. This paper proposes to model the pattern of this randomness values using random polynomial with unknown variables as coefficients. The behavior of the system will be obtained from zero distribution as the characteristic of such random polynomial. Results show that the pattern of random polynomial of avoiding collision can be constructed from zero distribution. Zero distribution is like building block of the system with obstacles as uncertainty factor. By scale factor k, which has range, the random coefficient pattern can be predicted.

Large-area imaging reveals biologically driven non-random spatial patterns of corals at a remote reef

Science.gov (United States)

Edwards, Clinton B.; Eynaud, Yoan; Williams, Gareth J.; Pedersen, Nicole E.; Zgliczynski, Brian J.; Gleason, Arthur C. R.; Smith, Jennifer E.; Sandin, Stuart A.

2017-12-01

For sessile organisms such as reef-building corals, differences in the degree of dispersion of individuals across a landscape may result from important differences in life-history strategies or may reflect patterns of habitat availability. Descriptions of spatial patterns can thus be useful not only for the identification of key biological and physical mechanisms structuring an ecosystem, but also by providing the data necessary to generate and test ecological theory. Here, we used an in situ imaging technique to create large-area photomosaics of 16 plots at Palmyra Atoll, central Pacific, each covering 100 m2 of benthic habitat. We mapped the location of 44,008 coral colonies and identified each to the lowest taxonomic level possible. Using metrics of spatial dispersion, we tested for departures from spatial randomness. We also used targeted model fitting to explore candidate processes leading to differences in spatial patterns among taxa. Most taxa were clustered and the degree of clustering varied by taxon. A small number of taxa did not significantly depart from randomness and none revealed evidence of spatial uniformity. Importantly, taxa that readily fragment or tolerate stress through partial mortality were more clustered. With little exception, clustering patterns were consistent with models of fragmentation and dispersal limitation. In some taxa, dispersion was linearly related to abundance, suggesting density dependence of spatial patterning. The spatial patterns of stony corals are non-random and reflect fundamental life-history characteristics of the taxa, suggesting that the reef landscape may, in many cases, have important elements of spatial predictability.

Spatial and temporal variability of guinea grass (Megathyrsus maximus) fuel loads and moisture on Oahu, Hawaii

Science.gov (United States)

Lisa M. Ellsworth; Creighton M. Litton; Andrew D. Taylor; J. Boone Kauffman

2013-01-01

Frequent wildfires in tropical landscapes dominated by non-native invasive grasses threaten surrounding ecosystems and developed areas. To better manage fire, accurate estimates of the spatial and temporal variability in fuels are urgently needed. We quantified the spatial variability in live and dead fine fuel loads and moistures at four guinea grass (...

Evaluating spatial and temporal variability in growth and mortality for recreational fisheries with limited catch data

Science.gov (United States)

Li, Yan; Wagner, Tyler; Jiao, Yan; Lorantas, Robert M.; Murphy, Cheryl

2018-01-01

Understanding the spatial and temporal variability in life-history traits among populations is essential for the management of recreational fisheries. However, valuable freshwater recreational fish species often suffer from a lack of catch information. In this study, we demonstrated the use of an approach to estimate the spatial and temporal variability in growth and mortality in the absence of catch data and apply the method to riverine smallmouth bass (Micropterus dolomieu) populations in Pennsylvania, USA. Our approach included a growth analysis and a length-based analysis that estimates mortality. Using a hierarchical Bayesian approach, we examined spatial variability in growth and mortality by assuming parameters vary spatially but remain constant over time and temporal variability by assuming parameters vary spatially and temporally. The estimated growth and mortality of smallmouth bass showed substantial variability over time and across rivers. We explored the relationships of the estimated growth and mortality with spring water temperature and spring flow. Growth rate was likely to be positively correlated with these two factors, while young mortality was likely to be positively correlated with spring flow. The spatially and temporally varying growth and mortality suggest that smallmouth bass populations across rivers may respond differently to management plans and disturbance such as environmental contamination and land-use change. The analytical approach can be extended to other freshwater recreational species that also lack of catch data. The approach could also be useful in developing population assessments with erroneous catch data or be used as a model sensitivity scenario to verify traditional models even when catch data are available.

Variable Selection in Time Series Forecasting Using Random Forests

Directory of Open Access Journals (Sweden)

Hristos Tyralis

2017-10-01

Full Text Available Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis

Science.gov (United States)

Wang, Jun; Wang, Yang; Zeng, Hui

2016-01-01

A key issue to address in synthesizing spatial data with variable-support in spatial analysis and modeling is the change-of-support problem. We present an approach for solving the change-of-support and variable-support data fusion problems. This approach is based on geostatistical inverse modeling that explicitly accounts for differences in spatial support. The inverse model is applied here to produce both the best predictions of a target support and prediction uncertainties, based on one or more measurements, while honoring measurements. Spatial data covering large geographic areas often exhibit spatial nonstationarity and can lead to computational challenge due to the large data size. We developed a local-window geostatistical inverse modeling approach to accommodate these issues of spatial nonstationarity and alleviate computational burden. We conducted experiments using synthetic and real-world raster data. Synthetic data were generated and aggregated to multiple supports and downscaled back to the original support to analyze the accuracy of spatial predictions and the correctness of prediction uncertainties. Similar experiments were conducted for real-world raster data. Real-world data with variable-support were statistically fused to produce single-support predictions and associated uncertainties. The modeling results demonstrate that geostatistical inverse modeling can produce accurate predictions and associated prediction uncertainties. It is shown that the local-window geostatistical inverse modeling approach suggested offers a practical way to solve the well-known change-of-support problem and variable-support data fusion problem in spatial analysis and modeling.

Generating Variable and Random Schedules of Reinforcement Using Microsoft Excel Macros

Science.gov (United States)

Bancroft, Stacie L.; Bourret, Jason C.

2008-01-01

Variable reinforcement schedules are used to arrange the availability of reinforcement following varying response ratios or intervals of time. Random reinforcement schedules are subtypes of variable reinforcement schedules that can be used to arrange the availability of reinforcement at a constant probability across number of responses or time.…

CONVERGENCE OF THE FRACTIONAL PARTS OF THE RANDOM VARIABLES TO THE TRUNCATED EXPONENTIAL DISTRIBUTION

Directory of Open Access Journals (Sweden)

Bogdan Gheorghe Munteanu

2013-01-01

Full Text Available Using the stochastic approximations, in this paper it was studiedthe convergence in distribution of the fractional parts of the sum of random variables to the truncated exponential distribution with parameter lambda. This fact is feasible by means of the Fourier-Stieltjes sequence (FSS of the random variable.

Random sets and random fuzzy sets as ill-perceived random variables an introduction for Ph.D. students and practitioners

CERN Document Server

Couso, Inés; Sánchez, Luciano

2014-01-01

This short book provides a unified view of the history and theory of random sets and fuzzy random variables, with special emphasis on its use for representing higher-order non-statistical uncertainty about statistical experiments. The authors lay bare the existence of two streams of works using the same mathematical ground, but differing form their use of sets, according to whether they represent objects of interest naturally taking the form of sets, or imprecise knowledge about such objects. Random (fuzzy) sets can be used in many fields ranging from mathematical morphology, economics, artificial intelligence, information processing and statistics per se, especially in areas where the outcomes of random experiments cannot be observed with full precision. This book also emphasizes the link between random sets and fuzzy sets with some techniques related to the theory of imprecise probabilities. This small book is intended for graduate and doctoral students in mathematics or engineering, but also provides an i...

A protocol for measuring spatial variables in soft-sediment tide pools

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Marina R. Brenha-Nunes

2016-01-01

Full Text Available ABSTRACT We present a protocol for measuring spatial variables in large (>50 m2 soft-sediment tide pool. Secondarily, we present the fish capture efficiency of a sampling protocol that based on such spatial variables to calculate relative abundances. The area of the pool is estimated by summing areas of basic geometric forms; the depth, by taken representative measurements of the depth variability of each pool's sector, previously determined according to its perimeter; and the volume, by considering the pool as a prism. These procedures were a trade-off between the acquisition of reliable estimates and the minimization of both the cost of operating and the time spent in field. The fish sampling protocol is based on two con secutive stages: 1 two people search for fishes under structures (e.g., rocks and litters on the pool and capture them with hand seines; 2 these structures are removed and then a beach-seine is hauled over the whole pool. Our method is cheaper than others and fast to operate considering the time in low tides. The method to sample fish is quite efficient resulting in a capture efficiency of 89%.

Output variability caused by random seeds in a multi-agent transport simulation model

DEFF Research Database (Denmark)

Paulsen, Mads; Rasmussen, Thomas Kjær; Nielsen, Otto Anker

2018-01-01

Dynamic transport simulators are intended to support decision makers in transport-related issues, and as such it is valuable that the random variability of their outputs is as small as possible. In this study we analyse the output variability caused by random seeds of a multi-agent transport...... simulator (MATSim) when applied to a case study of Santiago de Chile. Results based on 100 different random seeds shows that the relative accuracies of estimated link loads tend to increase with link load, but that relative errors of up to 10 % do occur even for links with large volumes. Although...

Hydrological and environmental variables outperform spatial factors in structuring species, trait composition, and beta diversity of pelagic algae.

Science.gov (United States)

Wu, Naicheng; Qu, Yueming; Guse, Björn; Makarevičiūtė, Kristė; To, Szewing; Riis, Tenna; Fohrer, Nicola

2018-03-01

There has been increasing interest in algae-based bioassessment, particularly, trait-based approaches are increasingly suggested. However, the main drivers, especially the contribution of hydrological variables, of species composition, trait composition, and beta diversity of algae communities are less studied. To link species and trait composition to multiple factors (i.e., hydrological variables, local environmental variables, and spatial factors) that potentially control species occurrence/abundance and to determine their relative roles in shaping species composition, trait composition, and beta diversities of pelagic algae communities, samples were collected from a German lowland catchment, where a well-proven ecohydrological modeling enabled to predict long-term discharges at each sampling site. Both trait and species composition showed significant correlations with hydrological, environmental, and spatial variables, and variation partitioning revealed that the hydrological and local environmental variables outperformed spatial variables. A higher variation of trait composition (57.0%) than species composition (37.5%) could be explained by abiotic factors. Mantel tests showed that both species and trait-based beta diversities were mostly related to hydrological and environmental heterogeneity with hydrological contributing more than environmental variables, while purely spatial impact was less important. Our findings revealed the relative importance of hydrological variables in shaping pelagic algae community and their spatial patterns of beta diversities, emphasizing the need to include hydrological variables in long-term biomonitoring campaigns and biodiversity conservation or restoration. A key implication for biodiversity conservation was that maintaining the instream flow regime and keeping various habitats among rivers are of vital importance. However, further investigations at multispatial and temporal scales are greatly needed.

Strong Laws of Large Numbers for Arrays of Rowwise NA and LNQD Random Variables

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Jiangfeng Wang

2011-01-01

Full Text Available Some strong laws of large numbers and strong convergence properties for arrays of rowwise negatively associated and linearly negative quadrant dependent random variables are obtained. The results obtained not only generalize the result of Hu and Taylor to negatively associated and linearly negative quadrant dependent random variables, but also improve it.

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

Science.gov (United States)

Szymanowski, Mariusz; Kryza, Maciej

2017-02-01

Our study examines the role of auxiliary variables in the process of spatial modelling and mapping of climatological elements, with air temperature in Poland used as an example. The multivariable algorithms are the most frequently applied for spatialization of air temperature, and their results in many studies are proved to be better in comparison to those obtained by various one-dimensional techniques. In most of the previous studies, two main strategies were used to perform multidimensional spatial interpolation of air temperature. First, it was accepted that all variables significantly correlated with air temperature should be incorporated into the model. Second, it was assumed that the more spatial variation of air temperature was deterministically explained, the better was the quality of spatial interpolation. The main goal of the paper was to examine both above-mentioned assumptions. The analysis was performed using data from 250 meteorological stations and for 69 air temperature cases aggregated on different levels: from daily means to 10-year annual mean. Two cases were considered for detailed analysis. The set of potential auxiliary variables covered 11 environmental predictors of air temperature. Another purpose of the study was to compare the results of interpolation given by various multivariable methods using the same set of explanatory variables. Two regression models: multiple linear (MLR) and geographically weighted (GWR) method, as well as their extensions to the regression-kriging form, MLRK and GWRK, respectively, were examined. Stepwise regression was used to select variables for the individual models and the cross-validation method was used to validate the results with a special attention paid to statistically significant improvement of the model using the mean absolute error (MAE) criterion. The main results of this study led to rejection of both assumptions considered. Usually, including more than two or three of the most significantly

Temporal and Spatial Variabilities of Japan Sea Surface Temperature and Atmospheric Forcings

National Research Council Canada - National Science Library

Chu, Peter C; Chen, Yuchun; Lu, Shihua

1998-01-01

...) and surface air temperature (SAT) data during 1982-1994 and the National Center for Atmospheric Research surface wind stress curl data during 1982-1989 to investigate the Japan Sea SST temporal and spatial variabilities...

Spatial variability of harmful algal blooms in Milford Lake, Kansas, July and August 2015

Science.gov (United States)

Foster, Guy M.; Graham, Jennifer L.; Stiles, Tom C.; Boyer, Marvin G.; King, Lindsey R.; Loftin, Keith A.

2017-01-09

Cyanobacterial harmful algal blooms (CyanoHABs) tend to be spatially variable vertically in the water column and horizontally across the lake surface because of in-lake and weather-driven processes and can vary by orders of magnitude in concentration across relatively short distances (meters or less). Extreme spatial variability in cyanobacteria and associated compounds poses unique challenges to collecting representative samples for scientific study and public-health protection. The objective of this study was to assess the spatial variability of cyanobacteria and microcystin in Milford Lake, Kansas, using data collected on July 27 and August 31, 2015. Spatially dense near-surface data were collected by the U.S. Geological Survey, nearshore data were collected by the Kansas Department of Health and Environment, and open-water data were collected by U.S. Army Corps of Engineers. CyanoHABs are known to be spatially variable, but that variability is rarely quantified. A better understanding of the spatial variability of cyanobacteria and microcystin will inform sampling and management strategies for Milford Lake and for other lakes with CyanoHAB issues throughout the Nation.The CyanoHABs in Milford Lake during July and August 2015 displayed the extreme spatial variability characteristic of cyanobacterial blooms. The phytoplankton community was almost exclusively cyanobacteria (greater than 90 percent) during July and August. Cyanobacteria (measured directly by cell counts and indirectly by regression-estimated chlorophyll) and microcystin (measured directly by enzyme-linked immunosorbent assay [ELISA] and indirectly by regression estimates) concentrations varied by orders of magnitude throughout the lake. During July and August 2015, cyanobacteria and microcystin concentrations decreased in the downlake (towards the outlet) direction.Nearshore and open-water surface grabs were collected and analyzed for microcystin as part of this study. Samples were collected in the

Extended q -Gaussian and q -exponential distributions from gamma random variables

Science.gov (United States)

Budini, Adrián A.

2015-05-01

The family of q -Gaussian and q -exponential probability densities fit the statistical behavior of diverse complex self-similar nonequilibrium systems. These distributions, independently of the underlying dynamics, can rigorously be obtained by maximizing Tsallis "nonextensive" entropy under appropriate constraints, as well as from superstatistical models. In this paper we provide an alternative and complementary scheme for deriving these objects. We show that q -Gaussian and q -exponential random variables can always be expressed as a function of two statistically independent gamma random variables with the same scale parameter. Their shape index determines the complexity q parameter. This result also allows us to define an extended family of asymmetric q -Gaussian and modified q -exponential densities, which reduce to the standard ones when the shape parameters are the same. Furthermore, we demonstrate that a simple change of variables always allows relating any of these distributions with a beta stochastic variable. The extended distributions are applied in the statistical description of different complex dynamics such as log-return signals in financial markets and motion of point defects in a fluid flow.

Spatial interpolation of climate variables in Northern Germany—Influence of temporal resolution and network density

Directory of Open Access Journals (Sweden)

C. Berndt

2018-02-01

New hydrological insights: Geostatistical techniques provide a better performance for all climate variables compared to simple methods Radar data improves the estimation of rainfall with hourly temporal resolution, while topography is useful for weekly to yearly values and temperature in general. No helpful information was found for cloudiness, sunshine duration, and wind speed, while interpolation of humidity benefitted from additional temperature data. The influences of temporal resolution, spatial variability, and additional information appear to be stronger than station density effects. High spatial variability of hourly precipitation causes the highest error, followed by wind speed, cloud coverage and sunshine duration. Lowest errors occur for temperature and humidity.

Spatial variability in intertidal macroalgal assemblages on the North Portuguese coast: consistence between species and functional group approaches

Science.gov (United States)

Veiga, P.; Rubal, M.; Vieira, R.; Arenas, F.; Sousa-Pinto, I.

2013-03-01

Natural assemblages are variable in space and time; therefore, quantification of their variability is imperative to identify relevant scales for investigating natural or anthropogenic processes shaping these assemblages. We studied the variability of intertidal macroalgal assemblages on the North Portuguese coast, considering three spatial scales (from metres to 10 s of kilometres) following a hierarchical design. We tested the hypotheses that (1) spatial pattern will be invariant at all the studied scales and (2) spatial variability of macroalgal assemblages obtained by using species will be consistent with that obtained using functional groups. This was done considering as univariate variables: total biomass and number of taxa as well as biomass of the most important species and functional groups and as multivariate variables the structure of macroalgal assemblages, both considering species and functional groups. Most of the univariate results confirmed the first hypothesis except for the total number of taxa and foliose macroalgae that showed significant variability at the scale of site and area, respectively. In contrast, when multivariate patterns were examined, the first hypothesis was rejected except at the scale of 10 s of kilometres. Both uni- and multivariate results indicated that variation was larger at the smallest scale, and thus, small-scale processes seem to have more effect on spatial variability patterns. Macroalgal assemblages, both considering species and functional groups as surrogate, showed consistent spatial patterns, and therefore, the second hypothesis was confirmed. Consequently, functional groups may be considered a reliable biological surrogate to study changes on macroalgal assemblages at least along the investigated Portuguese coastline.

Piecewise linearisation of the first order loss function for families of arbitrarily distributed random variables

NARCIS (Netherlands)

Rossi, R.; Hendrix, E.M.T.

2014-01-01

We discuss the problem of computing optimal linearisation parameters for the first order loss function of a family of arbitrarily distributed random variable. We demonstrate that, in contrast to the problem in which parameters must be determined for the loss function of a single random variable,

Spatial variability and trends of the rain intensity over Greece

Science.gov (United States)

Kambezidis, H. D.; Larissi, I. K.; Nastos, P. T.; Paliatsos, A. G.

2010-07-01

In this study, the spatial and temporal variability of the mean annual rain intensity in Greece are examined during a 41-year period (1962-2002). The meteorological datasets concern monthly rain amounts (mm) and the respective monthly durations (h) recorded at thirty two meteorological stations of the Hellenic National Meteorological Service, which are uniformly distributed on Greek territory, in order to calculate the mean monthly rain intensity. All the rain time series used in the analysis were tested by the application of the short-cut Bartlett test of homogeneity. The spatial distribution of the mean annual rain intensity is studied using the Kriging interpolation method, while the temporal variability, concerning the mean annual rain intensity trends along with their significance (Mann-Kendall test), is analysed. The findings of the analysis show that statistically significant negative trends (95% confidence level) appear mainly in the west sub-regions of Greece, while statistically significant positive trends (95% confidence level) appear in the wider area of Athens and the complex of Cyclades Islands. Further analysis concerning the seasonal rain intensity is needed, because there are different seasonal patterns, taking into account that, convective rain in Greece occurs mainly within the summer season.

Cryptographic pseudo-random sequence from the spatial chaotic map

International Nuclear Information System (INIS)

Sun Fuyan; Liu Shutang

2009-01-01

A scheme for pseudo-random binary sequence generation based on the spatial chaotic map is proposed. In order to face the challenge of using the proposed PRBS in cryptography, the proposed PRBS is subjected to statistical tests which are the well-known FIPS-140-1 in the area of cryptography, and correlation properties of the proposed sequences are investigated. The proposed PRBS successfully passes all these tests. Results of statistical testing of the sequences are found encouraging. The results of statistical tests suggest strong candidature for cryptographic applications.

Bayesian data fusion for spatial prediction of categorical variables in environmental sciences

Science.gov (United States)

Gengler, Sarah; Bogaert, Patrick

2014-12-01

First developed to predict continuous variables, Bayesian Maximum Entropy (BME) has become a complete framework in the context of space-time prediction since it has been extended to predict categorical variables and mixed random fields. This method proposes solutions to combine several sources of data whatever the nature of the information. However, the various attempts that were made for adapting the BME methodology to categorical variables and mixed random fields faced some limitations, as a high computational burden. The main objective of this paper is to overcome this limitation by generalizing the Bayesian Data Fusion (BDF) theoretical framework to categorical variables, which is somehow a simplification of the BME method through the convenient conditional independence hypothesis. The BDF methodology for categorical variables is first described and then applied to a practical case study: the estimation of soil drainage classes using a soil map and point observations in the sandy area of Flanders around the city of Mechelen (Belgium). The BDF approach is compared to BME along with more classical approaches, as Indicator CoKringing (ICK) and logistic regression. Estimators are compared using various indicators, namely the Percentage of Correctly Classified locations (PCC) and the Average Highest Probability (AHP). Although BDF methodology for categorical variables is somehow a simplification of BME approach, both methods lead to similar results and have strong advantages compared to ICK and logistic regression.

Bayesian data fusion for spatial prediction of categorical variables in environmental sciences

International Nuclear Information System (INIS)

Gengler, Sarah; Bogaert, Patrick

2014-01-01

First developed to predict continuous variables, Bayesian Maximum Entropy (BME) has become a complete framework in the context of space-time prediction since it has been extended to predict categorical variables and mixed random fields. This method proposes solutions to combine several sources of data whatever the nature of the information. However, the various attempts that were made for adapting the BME methodology to categorical variables and mixed random fields faced some limitations, as a high computational burden. The main objective of this paper is to overcome this limitation by generalizing the Bayesian Data Fusion (BDF) theoretical framework to categorical variables, which is somehow a simplification of the BME method through the convenient conditional independence hypothesis. The BDF methodology for categorical variables is first described and then applied to a practical case study: the estimation of soil drainage classes using a soil map and point observations in the sandy area of Flanders around the city of Mechelen (Belgium). The BDF approach is compared to BME along with more classical approaches, as Indicator CoKringing (ICK) and logistic regression. Estimators are compared using various indicators, namely the Percentage of Correctly Classified locations (PCC) and the Average Highest Probability (AHP). Although BDF methodology for categorical variables is somehow a simplification of BME approach, both methods lead to similar results and have strong advantages compared to ICK and logistic regression

An infinite-dimensional weak KAM theory via random variables

KAUST Repository

Gomes, Diogo A.

2016-08-31

We develop several aspects of the infinite-dimensional Weak KAM theory using a random variables\\' approach. We prove that the infinite-dimensional cell problem admits a viscosity solution that is a fixed point of the Lax-Oleinik semigroup. Furthermore, we show the existence of invariant minimizing measures and calibrated curves defined on R.

An infinite-dimensional weak KAM theory via random variables

KAUST Repository

Gomes, Diogo A.; Nurbekyan, Levon

2016-01-01

We develop several aspects of the infinite-dimensional Weak KAM theory using a random variables' approach. We prove that the infinite-dimensional cell problem admits a viscosity solution that is a fixed point of the Lax-Oleinik semigroup. Furthermore, we show the existence of invariant minimizing measures and calibrated curves defined on R.

Extensions of von Neumann's method for generating random variables

International Nuclear Information System (INIS)

Monahan, J.F.

1979-01-01

Von Neumann's method of generating random variables with the exponential distribution and Forsythe's method for obtaining distributions with densities of the form e/sup -G//sup( x/) are generalized to apply to certain power series representations. The flexibility of the power series methods is illustrated by algorithms for the Cauchy and geometric distributions

Field Scale Studies on the Spatial Variability of Soil Quality Indicators in Washington State, USA

Directory of Open Access Journals (Sweden)

Jeffrey L. Smith

2011-01-01

Full Text Available Arable lands are needed for sustainable agricultural systems to support an ever-growing human population. Soil quality needs to be defined to assure that new land brought into crop production is sustainable. To evaluate soil quality, a number of soil attributes will need to be measured, evaluated, and integrated into a soil-quality index using the multivariable indicator kriging (MVIK procedure. This study was conducted to determine the spatial variability and correlation of indicator parameters on a field scale with respect to soil quality and suitability for use with MVIK. The variability of the biological parameters decreased in the order of respiration > enzyme assays and qCO2 > microbial biomass C. The distribution frequency of all parameters except respiration were normal although the spatial distribution across the landscape was highly variable. The biological parameters showed little correlation with each other when all data points were considered; however, when grouped in smaller sections, the correlations were more consistent with observed patterns across the field. To accurately assess soil quality, and arable land use, consideration of spatial and temporal variability, soil conditions, and other controlling factors must be taken into account.

Effect of land use on the spatial variability of organic matter and nutrient status in an Oxisol

Science.gov (United States)

Paz-Ferreiro, Jorge; Alves, Marlene Cristina; Vidal Vázquez, Eva

2013-04-01

Heterogeneity is now considered as an inherent soil property. Spatial variability of soil attributes in natural landscapes results mainly from soil formation factors. In cultivated soils much heterogeneity can additionally occur as a result of land use, agricultural systems and management practices. Organic matter content (OMC) and nutrients associated to soil exchange complex are key attribute in the maintenance of a high quality soil. Neglecting spatial heterogeneity in soil OMC and nutrient status at the field scale might result in reduced yield and in environmental damage. We analyzed the impact of land use on the pattern of spatial variability of OMC and soil macronutrients at the stand scale. The study was conducted in São Paulo state, Brazil. Land uses were pasture, mango orchard and corn field. Soil samples were taken at 0-10 cm and 10-20 cm depth in 84 points, within 100 m x 100 m plots. Texture, pH, OMC, cation exchange capacity (CEC), exchangeable cations (Ca, Mg, K, H, Al) and resin extractable phosphorus were analyzed.. Statistical variability was found to be higher in parameters defining the soil nutrient status (resin extractable P, K, Ca and Mg) than in general soil properties (OMC, CEC, base saturation and pH). Geostatistical analysis showed contrasting patterns of spatial dependence for the different soil uses, sampling depths and studied properties. Most of the studied data sets collected at two different depths exhibited spatial dependence at the sampled scale and their semivariograms were modeled by a nugget effect plus a structure. The pattern of soil spatial variability was found to be different between the three study soil uses and at the two sampling depths, as far as model type, nugget effect or ranges of spatial dependence were concerned. Both statistical and geostatistical results pointed out the importance of OMC as a driver responsible for the spatial variability of soil nutrient status.

Spatial variability of surface fuels in treated and untreated ponderosa pine forests of the southern Rocky Mountains

Science.gov (United States)

Emma Vakili; Chad M. Hoffman; Robert E. Keane; Wade T. Tinkham; Yvette Dickinson

2016-01-01

There is growing consensus that spatial variability in fuel loading at scales down to 0.5 m may govern fire behaviour and effects. However, there remains a lack of understanding of how fuels vary through space in wildland settings. This study quantifies surface fuel loading and its spatial variability in ponderosa pine sites before and after fuels treatment in the...

How a dependent's variable non-randomness affects taper equation ...

African Journals Online (AJOL)

In order to apply the least squares method in regression analysis, the values of the dependent variable Y should be random. In an example of regression analysis linear and nonlinear taper equations, which estimate the diameter of the tree dhi at any height of the tree hi, were compared. For each tree the diameter at the ...

Documentation of programs that compute 1) static tilts for a spatially variable slip distribution, and 2) quasi-static tilts produced by an expanding dislocation loop with a spatially variable slip distribution

Science.gov (United States)

McHugh, Stuart

1976-01-01

The material in this report is concerned with the effects of a vertically oriented rectangular dislocation loop on the tilts observed at the free surface of an elastic half-space. Part I examines the effect of a spatially variable static strike-slip distribution across the slip surface. The tilt components as a function of distance parallel, or perpendicular, to the strike of the slip surface are displayed for different slip-versus-distance profiles. Part II examines the effect of spatially and temporally variable slip distributions across the dislocation loop on the quasi-static tilts at the free surface of an elastic half space. The model discussed in part II may be used to generate theoretical tilt versus time curves produced by creep events.

Spatial variability of caesium-137 activities in soils in the Jura mountains

International Nuclear Information System (INIS)

Pimou-Heumou, G.; Lucot, E.; Crini, N.; Briot, M.; Badot, P.M.

2011-01-01

275 soil samples were taken in the catchment area of the upper part of the Doubs river located in the Jura mountains according to a sampling strategy designed to evaluate the extent of the spatial variability of 137 Cs activities and to identify its main sources. 137 Cs activities ranged between about 1000 and 12000 Bq.m -2 with an average of approximately 3600 Bq.m -2 . The spatial variability of the contamination is high: 137 Cs activity shows statistically significant links with altitude, soil organic matter and land cover, whereas the other studied parameters, i.e. soil type and topographic position, do not constitute significant sources of variation. These results are discussed in terms of evaluation of the radioactive contamination on a regional scale. They show that to be satisfactory, a sampling strategy must necessarily take into account the various types of land cover. (authors)

Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review

OpenAIRE

E. Cristiano; M.-C. ten Veldhuis; N. van de Giesen

2017-01-01

In urban areas, hydrological processes are characterized by high variability in space and time, making them sensitive to small-scale temporal and spatial rainfall variability. In the last decades new instruments, techniques, and methods have been developed to capture rainfall and hydrological processes at high resolution. Weather radars have been introduced to estimate high spatial and temporal rainfall variability. At the same time, new models have been proposed to reproduce hydrological res...

The spatial heterogeneity between Japanese encephalitis incidence distribution and environmental variables in Nepal.

Directory of Open Access Journals (Sweden)

Daniel E Impoinvil

Full Text Available To identify potential environmental drivers of Japanese Encephalitis virus (JE transmission in Nepal, we conducted an ecological study to determine the spatial association between 2005 Nepal JE incidence, and climate, agricultural, and land-cover variables at district level.District-level data on JE cases were examined using Local Indicators of Spatial Association (LISA analysis to identify spatial clusters from 2004 to 2008 and 2005 data was used to fit a spatial lag regression model with climate, agriculture and land-cover variables.Prior to 2006, there was a single large cluster of JE cases located in the Far-West and Mid-West terai regions of Nepal. After 2005, the distribution of JE cases in Nepal shifted with clusters found in the central hill areas. JE incidence during the 2005 epidemic had a stronger association with May mean monthly temperature and April mean monthly total precipitation compared to mean annual temperature and precipitation. A parsimonious spatial lag regression model revealed, 1 a significant negative relationship between JE incidence and April precipitation, 2 a significant positive relationship between JE incidence and percentage of irrigated land 3 a non-significant negative relationship between JE incidence and percentage of grassland cover, and 4 a unimodal non-significant relationship between JE Incidence and pig-to-human ratio.JE cases clustered in the terai prior to 2006 where it seemed to shift to the Kathmandu region in subsequent years. The spatial pattern of JE cases during the 2005 epidemic in Nepal was significantly associated with low precipitation and the percentage of irrigated land. Despite the availability of an effective vaccine, it is still important to understand environmental drivers of JEV transmission since the enzootic cycle of JEV transmission is not likely to be totally interrupted. Understanding the spatial dynamics of JE risk factors may be useful in providing important information to the

Temporal and spatial variability in thalweg profiles of a gravel-bed river

Science.gov (United States)

Madej, Mary Ann

1999-01-01

This study used successive longitudinal thalweg profiles in gravel-bed rivers to monitor changes in bed topography following floods and associated large sediment inputs. Variations in channel bed elevations, distributions of residual water depths, percentage of channel length occupied by riffles, and a spatial autocorrelation coefficient (Moran's I) were used to quantify changes in morphological diversity and spatial structure in Redwood Creek basin, northwestern California. Bed topography in Redwood Creek and its major tributaries consists primarily of a series of pools and riffles. The size, frequency and spatial distribution of the pools and riffles have changed significantly during the past 20 years. Following large floods and high sediment input in Redwood Creek and its tributaries in 1975, variation in channel bed elevations was low and the percentage of the channel length occupied by riffles was high. Over the next 20 years, variation in bed elevations increased while the length of channel occupied by riffles decreased. An index [(standard deviation of residual water depth/bankfull depth) × 100] was developed to compare variations in bed elevation over a range of stream sizes, with a higher index being indicative of greater morphological diversity. Spatial autocorrelation in the bed elevation data was apparent at both fine and coarse scales in many of the thalweg profiles and the observed spatial pattern of bed elevations was found to be related to the dominant channel material and the time since disturbance. River reaches in which forced pools dominated, and in which large woody debris and bed particles could not be easily mobilized, exhibited a random distribution of bed elevations. In contrast, in reaches where alternate bars dominated, and both wood and gravel were readily transported, regularly spaced bed topography developed at a spacing that increased with time since disturbance. This pattern of regularly spaced bed features was reversed

Approximating prediction uncertainty for random forest regression models

Science.gov (United States)

John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne

2016-01-01

Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as...

Spatial Variability of Indicators of Jiaokou Reservoir Under Different Sampling Scales

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WEI Wen-juan

2016-12-01

Full Text Available This research determined total nitrogen, total phosphorus, ammonia nitrogen and potassium permanganate contents in different scales of Jiaokou reservoir with the purpose of exploring the applicability of spatial variability and its characteristic in different sampling scales. The results showed that, compared the sampling scales of 100 m with 200 m, there were some differences among four indicators in the spatial variation, interpolation simulation and spatial distribution. About the testing model fit, the fitting model for the total nitrogen, permanganate index was Gaussian model, the fitting model for total phosphorus, ammonia nitrogen was the spherical model; Combining evaluation of parameters of models and comprehensive evaluation of spatial interpolation, total nitrogen, total phosphorus showed stronger spatial correlation and better interpolation simulation quality on the sampling scales of 200 m, while total phosphorus and permanganate index showed certain advantages on the 100 m scale; On the aspect of spatial distributions, the contents of ammonia nitrogen and potassium permanganate were mainly affected by human factors, the total phosphorus was affected by internal factors of the reservoir, while total nitrogen was closely related to farming activities around reservoir. The above results showed that total nitrogen, ammonia nitrogen were more available for the 200 m scales and total phosphorus, potassium permanganate were more available for the 100 m scales.

SOERP, Statistics and 2. Order Error Propagation for Function of Random Variables

International Nuclear Information System (INIS)

Cox, N. D.; Miller, C. F.

1985-01-01

1 - Description of problem or function: SOERP computes second-order error propagation equations for the first four moments of a function of independently distributed random variables. SOERP was written for a rigorous second-order error propagation of any function which may be expanded in a multivariable Taylor series, the input variables being independently distributed. The required input consists of numbers directly related to the partial derivatives of the function, evaluated at the nominal values of the input variables and the central moments of the input variables from the second through the eighth. 2 - Method of solution: The development of equations for computing the propagation of errors begins by expressing the function of random variables in a multivariable Taylor series expansion. The Taylor series expansion is then truncated, and statistical operations are applied to the series in order to obtain equations for the moments (about the origin) of the distribution of the computed value. If the Taylor series is truncated after powers of two, the procedure produces second-order error propagation equations. 3 - Restrictions on the complexity of the problem: The maximum number of component variables allowed is 30. The IBM version will only process one set of input data per run

Monitoring temporal and spatial variability in sandeel (Ammodytes hexapterus) abundance with pigeon guillemot (Cepphus columba) diets

Science.gov (United States)

Litzow, Michael A.; Piatt, John F.; Abookire, Alisa A.; Prichard, A.K.; Robards, Martin D.

2000-01-01

We evaluated pigeon guillemots (Cepphus columba) as monitors of nearshore fish abundance and community composition during 1995-1999 at Kachemak Bay, Alaska. We studied the composition of chick diets at 10 colonies and simultaneously measured fish abundance around colonies with beach seines and bottom trawls. Sandeels (Ammodytes hexapterus) formed the majority of the diet at one group of colonies. Temporal variability in sandeel abundance explained 74% of inter-annual variability in diet composition at these colonies and 93% of seasonal variability. Diets at other colonies were dominated by demersal fish. Among these colonies, 81% of the variability in the proportion of sandeels in diets was explained by spatial differences in sanded abundance. Pigeon guillemots exhibited a non-linear functional response to sandeel abundance in the area where these fish were most abundant. Temporal and spatial variability in demersal fish abundance was not consistently reflected in diets. Spatial differences in the proportion of different demersal fishes in the diet may have been driven by differences in guillemot prey preference. Prey specialization by individual pigeon guillemots was common, and may operate at the colony level. Inter-annual variability in sandeel abundance may have been tracked more accurately because the magnitude of change (11-fold) was greater than that of demersal fish (three-fold). (C) 2000 International Council for the Exploration of the Sea.

Systems, methods, and software for determining spatially variable distributions of the dielectric properties of a heterogeneous material

Science.gov (United States)

Farrington, Stephen P.

2018-05-15

Systems, methods, and software for measuring the spatially variable relative dielectric permittivity of materials along a linear or otherwise configured sensor element, and more specifically the spatial variability of soil moisture in one dimension as inferred from the dielectric profile of the soil matrix surrounding a linear sensor element. Various methods provided herein combine advances in the processing of time domain reflectometry data with innovations in physical sensing apparatuses. These advancements enable high temporal (and thus spatial) resolution of electrical reflectance continuously along an insulated waveguide that is permanently emplaced in contact with adjacent soils. The spatially resolved reflectance is directly related to impedance changes along the waveguide that are dominated by electrical permittivity contrast due to variations in soil moisture. Various methods described herein are thus able to monitor soil moisture in profile with high spatial resolution.

Higher order moments of a sum of random variables: remarks and applications.

Directory of Open Access Journals (Sweden)

Luisa Tibiletti

1996-02-01

Full Text Available The moments of a sum of random variables depend on both the pure moments of each random addendum and on the addendum mixed moments. In this note we introduce a simple measure to evaluate the relative impedance to attach to the latter. Once the pure moments are fixed, the functional relation between the random addenda leading to the extreme values is also provided. Applications to Finance, Decision Theory and Actuarial Sciences are also suggested.

Industrial implementation of spatial variability control by real-time SPC

Science.gov (United States)

Roule, O.; Pasqualini, F.; Borde, M.

2016-10-01

Advanced technology nodes require more and more information to get the wafer process well setup. The critical dimension of components decreases following Moore's law. At the same time, the intra-wafer dispersion linked to the spatial non-uniformity of tool's processes is not capable to decrease in the same proportions. APC systems (Advanced Process Control) are being developed in waferfab to automatically adjust and tune wafer processing, based on a lot of process context information. It can generate and monitor complex intrawafer process profile corrections between different process steps. It leads us to put under control the spatial variability, in real time by our SPC system (Statistical Process Control). This paper will outline the architecture of an integrated process control system for shape monitoring in 3D, implemented in waferfab.

Generation of correlated finite alphabet waveforms using gaussian random variables

KAUST Repository

Ahmed, Sajid

2016-01-13

Various examples of methods and systems are provided for generation of correlated finite alphabet waveforms using Gaussian random variables in, e.g., radar and communication applications. In one example, a method includes mapping an input signal comprising Gaussian random variables (RVs) onto finite-alphabet non-constant-envelope (FANCE) symbols using a predetermined mapping function, and transmitting FANCE waveforms through a uniform linear array of antenna elements to obtain a corresponding beampattern. The FANCE waveforms can be based upon the mapping of the Gaussian RVs onto the FANCE symbols. In another example, a system includes a memory unit that can store a plurality of digital bit streams corresponding to FANCE symbols and a front end unit that can transmit FANCE waveforms through a uniform linear array of antenna elements to obtain a corresponding beampattern. The system can include a processing unit that can encode the input signal and/or determine the mapping function.

Generation of correlated finite alphabet waveforms using gaussian random variables

KAUST Repository

Ahmed, Sajid; Alouini, Mohamed-Slim; Jardak, Seifallah

2016-01-01

Various examples of methods and systems are provided for generation of correlated finite alphabet waveforms using Gaussian random variables in, e.g., radar and communication applications. In one example, a method includes mapping an input signal comprising Gaussian random variables (RVs) onto finite-alphabet non-constant-envelope (FANCE) symbols using a predetermined mapping function, and transmitting FANCE waveforms through a uniform linear array of antenna elements to obtain a corresponding beampattern. The FANCE waveforms can be based upon the mapping of the Gaussian RVs onto the FANCE symbols. In another example, a system includes a memory unit that can store a plurality of digital bit streams corresponding to FANCE symbols and a front end unit that can transmit FANCE waveforms through a uniform linear array of antenna elements to obtain a corresponding beampattern. The system can include a processing unit that can encode the input signal and/or determine the mapping function.

The Bayesian group lasso for confounded spatial data

Science.gov (United States)

Hefley, Trevor J.; Hooten, Mevin B.; Hanks, Ephraim M.; Russell, Robin E.; Walsh, Daniel P.

2017-01-01

Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM.

Spatial variability of soil potassium in sugarcane areas subjected to the application of vinasse

Directory of Open Access Journals (Sweden)

LAÉRCIO A. DE CARVALHO

2014-12-01

Full Text Available When deposited on land the vinasse can promote improvement in fertility, however, often fertilizer application occurs in areas considered homogeneous, without taking into account the variability of the soil. The objective of this study was to evaluate the effect of vinasse application on potassium content in two classes of soils cultivated with sugarcane, and characterize the spatial variability of soil using geostatistical techniques. In the 2010 and 2011 crop year, soil samples were collected from an experimental grid at 0-0.2 and 0.2-0.4 m depth in three soils cultivated with sugarcane, totaling 90 samplings in each grid, for the determination of pH, calcium (Ca, magnesium (Mg, potassium (K, phosphorus (P, aluminum (Al and potential acidity (H + Al. The data have been submitted to analysis of descriptive statistics and the K attribute was subjected to geostatistical analysis. The coefficient of variation indicated medium and high variability of K for the three soils. The results showed that the spatial dependence of K increased in depth to FRce and decreased to PHlv, indicating that the attribute could have followed the pattern of distribution of clay in depth. The investigation of the spatial variability of K on the surface and subsurface soils provided the definition of management zones with different levels of fertility, which can be organized into sub-areas for a more efficient management of the resources and the environment.

The geological basis and the representation of spatial variability in fractured media

International Nuclear Information System (INIS)

Mazurek, M.; Gautschi, A.; Zuidema, P.

1998-01-01

Spatial variability of features and parameters relevant for contaminant transport modelling occurs on all scales of interest for the quantification of processes that govern solute migration, typically decimeters to hundreds of meters. Two types of spatial variability are distinguished, namely the internal heterogeneity of each individual water-conducting feature (e.g. the complex architecture of a fault) and the larger-scale heterogeneity that results from the groundwater flow through different types of water-conducting features along the flow-path from the repository to the discharge areas. An up-scaling procedure is required to obtain hydraulic parameters and the properties of the overall flow-path, whereas the heterogeneity of many other geologic features (geometry of flow and matrix porosity, mineralogy, etc.) can be fed directly into coupled codes that quantify radionuclide transport. The procedures needed to derive conceptual models integrating geological and hydraulic field measurements and observations at a given site are illustrated by examples from both crystalline and sedimentary rock formations. (author)

Spatial and temporal variability of chorus and hiss

Science.gov (United States)

Santolik, O.; Hospodarsky, G. B.; Kurth, W. S.; Kletzing, C.

2017-12-01

Whistler-mode electromagnetic waves, especially natural emissions of chorus and hiss, have been shown to influence the dynamics of the Van Allen radiation belts via quasi-linear or nonlinear wave particle interactions, transferring energy between different electron populations. Average intensities of chorus and hiss emissions have been found to increase with increasing levels of geomagnetic activity but their stochastic variations in individual spacecraft measurements are usually larger these large-scale temporal effects. To separate temporal and spatial variations of wave characteristics, measurements need to be simultaneously carried out in different locations by identical and/or well calibrated instrumentation. We use two-point survey measurements of the Waves instruments of the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) onboard two Van Allen Probes to asses spatial and temporal variability of chorus and hiss. We take advantage of a systematic analysis of this large data set which has been collected during 2012-2017 over a range of separation vectors of the two spacecraft. We specifically address the question whether similar variations occur at different places at the same time. Our results indicate that power variations are dominated by separations in MLT at scales larger than 0.5h.

A preliminary characterization of the spatial variability of precipitation at Yucca Mountain, Nevada

International Nuclear Information System (INIS)

Hevesi, J.A.; Flint, A.L.; Ambos, D.S.

1994-01-01

Isohyetal maps of precipitation and numerical models for simulating precipitation are needed to help characterize natural infiltration at Yucca Mountain, Nevada. A geostatistical analysis of measured precipitation accumulated from storm periods. Precipitation was measured during a 3.8 year period from January 1990 to October, 1993 using a network of precipitation gages. A total of 34 winter-type storms and 12 summer-type storm, categorized using synoptic weather records, were analyzed using the 1st and 2nd statistical moments and sample variograms. Average standardized variograms indicated good spatial correlation for both storm types with only slight differences in the general spatial structure. Coefficients of variation and average relative variograms indicated that summer storms are characterized by greater variability as compared to winter storms. Models were fitted to the average summer and winter standarized variograms for each storm using the mean storm depth and the coefficient of variation as scaling parameters. Isohyetal maps of 4 representative storms were created using the standarized models. Results indicate that standarized models can be used to simulate the spatial distribution of precipitation depth, provided that the 1st and 2nd moments are known or can be estimated, and that identifiable deterministic trends can be included in the models. A single, fixed model representing the spatial variability of precipitation at Yucca Mountain is not recommended

Variabilidad espacial y diaria del contenido de humedad en el suelo en tres sistemas agroforestales Spatial and daily variability of soil moisture content in three agroforestry systems

Directory of Open Access Journals (Sweden)

Mariela Rivera Peña

2009-04-01

Full Text Available En seis puntos de tres transectos (102 m paralelos (9 m en tres sistemas de uso del terreno (Quesungual menor de dos años, SAQThe objective of this study was to determine the level of soil spatial variability in an area consisting of the land uses: Quesungual slash and mulch agroforestry system with less than two years (QSMAS<2, Slash-and-burn traditional system (SB and Secondary forest (SF. Soil samples were taken in three parallel transects of 102 m in length, separated 9 meters. The profile was sampled in the depths from 0 to 5 cm, 5 to 10 cm, 10 to 20 cm and 20 to 40 cm in 6 points (09, 11 am and 05 during 9 days. Coefficient of variation for soil properties varied for bulk density (0.76 and 15.1%, organic carbon (30.4 and 54.3%, volumetric moisture (9.5 and 23.5%, sand (12.8 and 22.5% and clay (14.0 and 29.2%. The geo-statistical analysis showed that the random component of the spatial dependence was predominant over the nugget effect. The functions of semivariograms, structured for each variable were used to generate maps of interpolated contours at a fine scale. The Moran (I autocorrelation indicated that sampling ranges less than 9 m would be adequate to detect spatial structure of the volumetric moisture variable.

Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.

Science.gov (United States)

Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R

2015-04-01

Climate variability, the primary expression of climate change, is one of the most important environmental problems affecting human health, particularly vector-borne diseases. Despite research efforts worldwide, there are few studies addressing the use of information on climate variability for prevention and early warning of vector-borne infectious diseases. Show the utility of climate information for vector surveillance by developing spatial models using an entomological indicator and information on predicted climate variability in Cuba to provide early warning of danger of increased risk of dengue transmission. An ecological study was carried out using retrospective and prospective analyses of time series combined with spatial statistics. Several entomological and climatic indicators were considered using complex Bultó indices -1 and -2. Moran's I spatial autocorrelation coefficient specified for a matrix of neighbors with a radius of 20 km, was used to identify the spatial structure. Spatial structure simulation was based on simultaneous autoregressive and conditional autoregressive models; agreement between predicted and observed values for number of Aedes aegypti foci was determined by the concordance index Di and skill factor Bi. Spatial and temporal distributions of populations of Aedes aegypti were obtained. Models for describing, simulating and predicting spatial patterns of Aedes aegypti populations associated with climate variability patterns were put forward. The ranges of climate variability affecting Aedes aegypti populations were identified. Forecast maps were generated for the municipal level. Using the Bultó indices of climate variability, it is possible to construct spatial models for predicting increased Aedes aegypti populations in Cuba. At 20 x 20 km resolution, the models are able to provide warning of potential changes in vector populations in rainy and dry seasons and by month, thus demonstrating the usefulness of climate information for

The Weakest Link : Spatial Variability in the Piping Failure Mechanism of Dikes

NARCIS (Netherlands)

Kanning, W.

2012-01-01

Piping is an important failure mechanism of flood defense structures. A dike fails due to piping when a head difference causes first the uplift of an inland blanket layer, and subsequently soil erosion due to a ground water flow. Spatial variability of subsoil parameters causes the probability of

Tolerance limits and tolerance intervals for ratios of normal random variables using a bootstrap calibration.

Science.gov (United States)

Flouri, Marilena; Zhai, Shuyan; Mathew, Thomas; Bebu, Ionut

2017-05-01

This paper addresses the problem of deriving one-sided tolerance limits and two-sided tolerance intervals for a ratio of two random variables that follow a bivariate normal distribution, or a lognormal/normal distribution. The methodology that is developed uses nonparametric tolerance limits based on a parametric bootstrap sample, coupled with a bootstrap calibration in order to improve accuracy. The methodology is also adopted for computing confidence limits for the median of the ratio random variable. Numerical results are reported to demonstrate the accuracy of the proposed approach. The methodology is illustrated using examples where ratio random variables are of interest: an example on the radioactivity count in reverse transcriptase assays and an example from the area of cost-effectiveness analysis in health economics. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Possibility/Necessity-Based Probabilistic Expectation Models for Linear Programming Problems with Discrete Fuzzy Random Variables

Directory of Open Access Journals (Sweden)

Hideki Katagiri

2017-10-01

Full Text Available This paper considers linear programming problems (LPPs where the objective functions involve discrete fuzzy random variables (fuzzy set-valued discrete random variables. New decision making models, which are useful in fuzzy stochastic environments, are proposed based on both possibility theory and probability theory. In multi-objective cases, Pareto optimal solutions of the proposed models are newly defined. Computational algorithms for obtaining the Pareto optimal solutions of the proposed models are provided. It is shown that problems involving discrete fuzzy random variables can be transformed into deterministic nonlinear mathematical programming problems which can be solved through a conventional mathematical programming solver under practically reasonable assumptions. A numerical example of agriculture production problems is given to demonstrate the applicability of the proposed models to real-world problems in fuzzy stochastic environments.

Throughfall and its spatial variability beneath xerophytic shrub canopies within water-limited arid desert ecosystems

Science.gov (United States)

Zhang, Ya-feng; Wang, Xin-ping; Hu, Rui; Pan, Yan-xia

2016-08-01

Throughfall is known to be a critical component of the hydrological and biogeochemical cycles of forested ecosystems with inherently temporal and spatial variability. Yet little is understood concerning the throughfall variability of shrubs and the associated controlling factors in arid desert ecosystems. Here we systematically investigated the variability of throughfall of two morphological distinct xerophytic shrubs (Caragana korshinskii and Artemisia ordosica) within a re-vegetated arid desert ecosystem, and evaluated the effects of shrub structure and rainfall characteristics on throughfall based on heavily gauged throughfall measurements at the event scale. We found that morphological differences were not sufficient to generate significant difference (P < 0.05) in throughfall between two studied shrub species under the same rainfall and meteorological conditions in our study area, with a throughfall percentage of 69.7% for C. korshinskii and 64.3% for A. ordosica. We also observed a highly variable patchy pattern of throughfall beneath individual shrub canopies, but the spatial patterns appeared to be stable among rainfall events based on time stability analysis. Throughfall linearly increased with the increasing distance from the shrub base for both shrubs, and radial direction beneath shrub canopies had a pronounced impact on throughfall. Throughfall variability, expressed as the coefficient of variation (CV) of throughfall, tended to decline with the increase in rainfall amount, intensity and duration, and stabilized passing a certain threshold. Our findings highlight the great variability of throughfall beneath the canopies of xerophytic shrubs and the time stability of throughfall pattern among rainfall events. The spatially heterogeneous and temporally stable throughfall is expected to generate a dynamic patchy distribution of soil moisture beneath shrub canopies within arid desert ecosystems.

Spatial Scaling of Environmental Variables Improves Species-Habitat Models of Fishes in a Small, Sand-Bed Lowland River.

Directory of Open Access Journals (Sweden)

Johannes Radinger

Full Text Available Habitat suitability and the distinct mobility of species depict fundamental keys for explaining and understanding the distribution of river fishes. In recent years, comprehensive data on river hydromorphology has been mapped at spatial scales down to 100 m, potentially serving high resolution species-habitat models, e.g., for fish. However, the relative importance of specific hydromorphological and in-stream habitat variables and their spatial scales of influence is poorly understood. Applying boosted regression trees, we developed species-habitat models for 13 fish species in a sand-bed lowland river based on river morphological and in-stream habitat data. First, we calculated mean values for the predictor variables in five distance classes (from the sampling site up to 4000 m up- and downstream to identify the spatial scale that best predicts the presence of fish species. Second, we compared the suitability of measured variables and assessment scores related to natural reference conditions. Third, we identified variables which best explained the presence of fish species. The mean model quality (AUC = 0.78, area under the receiver operating characteristic curve significantly increased when information on the habitat conditions up- and downstream of a sampling site (maximum AUC at 2500 m distance class, +0.049 and topological variables (e.g., stream order were included (AUC = +0.014. Both measured and assessed variables were similarly well suited to predict species' presence. Stream order variables and measured cross section features (e.g., width, depth, velocity were best-suited predictors. In addition, measured channel-bed characteristics (e.g., substrate types and assessed longitudinal channel features (e.g., naturalness of river planform were also good predictors. These findings demonstrate (i the applicability of high resolution river morphological and instream-habitat data (measured and assessed variables to predict fish presence, (ii the

Determining the spatial variability of personal sampler inlet locations.

Science.gov (United States)

Vinson, Robert; Volkwein, Jon; McWilliams, Linda

2007-09-01

This article examines the spatial variability of dust concentrations within a coal miner's breathing zone and the impact of sampling location at the cap lamp, nose, and lapel. Tests were conducted in the National Institute for Safety and Health Pittsburgh Research Laboratory full-scale, continuous miner gallery using three prototype personal dust monitors (PDM). The dust masses detected by the PDMs were used to calculate the percentage difference of dust mass between the cap lamp and the nose and between the lapel and the nose. The calculated percentage differences of the masses ranged from plus 12% to minus 25%. Breathing zone tests were also conducted in four underground coal mines using the torso of a mannequin to simulate a miner. Coal mine dust was sampled with multi-cyclone sampling cans mounted directly in front of the mannequin near the cap lamp, nose, and lapel. These four coal mine tests found that the spatial variability of dust levels and imprecision of the current personal sampler is a greater influence than the sampler location within the breathing zone. However, a one-sample t-test of this data did find that the overall mean value of the cap lamp/nose ratio was not significantly different than 1 (p-value = 0.21). However, when applied to the overall mean value of the lapel/nose ratio there was a significant difference from 1 (p-value sampling location for coal mine dust samples. But these results suggest that the cap location is slightly more indicative of what is breathed through the nose area.

The spatial impact of neighbouring on the exports activities of COMESA countries by using spatial panel models

Science.gov (United States)

Hamzalouh, L.; Ismail, M. T.; Rahman, R. A.

2017-09-01

In this paper, spatial panel models were used and the method for selecting the best model amongst the spatial fixed effects model and the spatial random effects model to estimate the fitting model by using the robust Hausman test for analysis of the exports pattern of the Common Market for Eastern and Southern African (COMESA) countries. And examine the effects of the interactions of the economic statistic of explanatory variables on the exports of the COMESA. Results indicated that the spatial Durbin model with fixed effects specification should be tested and considered in most cases of this study. After that, the direct and indirect effects among COMESA regions were assessed, and the role of indirect spatial effects in estimating exports was empirically demonstrated. Regarding originality and research value, and to the best of the authors’ knowledge, this is the first attempt to examine exports between COMESA and its member countries through spatial panel models using XSMLE, which is a new command for spatial analysis using STATA.

Quantification of the spatial variability of rainfall based on a dense network of rain gauges

DEFF Research Database (Denmark)

Pedersen, Lisbeth; Jensen, Niels Einar; Christiansen, Lasse Engbo

2010-01-01

The spatial variability of rainfall within a single Local Area Weather Radar (LAWR) pixel of 500 x 500 m is quantified based on data from two locations. The work was motivated by the need to quantify the variability on this scale in order to provide an estimate of the uncertainty of using a single...... from an earlier campaign in 2003. The fact that the 20072008 dataset was almost four times larger than the original dataset from 2003 motivated this extended study. Two methods were used to describe the variability: the coefficient of variation and the spatial correlation structure of the rainfall......% prediction interval for a given rainfall depth is estimated and can be used to address the uncertainty of using a single rain gauge to represent the rainfall within a 500 x 500 m area. (C) 2009 Elsevier B.V. All rights reserved....

Spatial variability of atrazine dissipation in an allophanic soil.

Science.gov (United States)

Müller, Karin; Smith, Roger E; James, Trevor K; Holland, Patrick T; Rahman, Anis

2003-08-01

The small-scale variability (0.5 m) of atrazine (6-chloro-N2-ethyl-N4-isopropyl-1,3,5-triazine-2,4-diamine) concentrations and soil water contents in a volcanic silt loam soil (Haplic Andosol, FAO system) was studied in an area of 0.1 ha. Descriptive and spatial statistics were used to analyse the data. On average we recovered 102% of the applied atrazine 2 h after the herbicide application (CV = 35%). An increase in the CV of the concentrations with depth could be ascribed to a combination of extrinsic and intrinsic factors. Both variables, atrazine concentrations and soil water content, showed a high horizontal variability. The semivariograms of the atrazine concentrations exhibited the pure nugget effect, no pattern could be determined along the 15.5-m long transects on any of the seven sampling days over a 55-day period. Soil water content had a weak spatial autocorrelation with a range of 6-10 m. The dissipation of atrazine analysed using a high vertical sampling resolution of 0.02 m to 0.2 m showed that 70% of the applied atrazine persisted in the upper 0.02-m layer of the soil for 12 days. After 55 days and 410 mm of rainfall the centre of the pesticide mass was still at a soil depth of 0.021 m. The special characteristics of the soil (high organic carbon content, allophanic clay) had a strong influence on atrazine sorption and mobility. The mass recovery after 55 days was low. The laboratory degradation rate for atrazine, determined in a complementary incubation study and corrected for the actual field temperature using the Arrhenius equation, only accounted for about 35% of the losses that occurred in the field. Results suggest field degradation rates to be more changeable in time and much faster than under controlled conditions. Preferential flow is discussed as a component of the field transport process.

The effects of environmental variability and spatial sampling on the three-dimensional inversion problem.

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Bender, Christopher M; Ballard, Megan S; Wilson, Preston S

2014-06-01

The overall goal of this work is to quantify the effects of environmental variability and spatial sampling on the accuracy and uncertainty of estimates of the three-dimensional ocean sound-speed field. In this work, ocean sound speed estimates are obtained with acoustic data measured by a sparse autonomous observing system using a perturbative inversion scheme [Rajan, Lynch, and Frisk, J. Acoust. Soc. Am. 82, 998-1017 (1987)]. The vertical and horizontal resolution of the solution depends on the bandwidth of acoustic data and on the quantity of sources and receivers, respectively. Thus, for a simple, range-independent ocean sound speed profile, a single source-receiver pair is sufficient to estimate the water-column sound-speed field. On the other hand, an environment with significant variability may not be fully characterized by a large number of sources and receivers, resulting in uncertainty in the solution. This work explores the interrelated effects of environmental variability and spatial sampling on the accuracy and uncertainty of the inversion solution though a set of case studies. Synthetic data representative of the ocean variability on the New Jersey shelf are used.

Spatial variability of shortwave radiative fluxes in the context of snowmelt

Science.gov (United States)

Pinker, Rachel T.; Ma, Yingtao; Hinkelman, Laura; Lundquist, Jessica

2014-05-01

Snow-covered mountain ranges are a major source of water supply for run-off and groundwater recharge. Snowmelt supplies as much as 75% of surface water in basins of the western United States. Factors that affect the rate of snow melt include incoming shortwave and longwave radiation, surface albedo, snow emissivity, snow surface temperature, sensible and latent heat fluxes, ground heat flux, and energy transferred to the snowpack from deposited snow or rain. The net radiation generally makes up about 80% of the energy balance and is dominated by the shortwave radiation. Complex terrain poses a great challenge for obtaining the needed information on radiative fluxes from satellites due to elevation issues, spatially-variable cloud cover, rapidly changing surface conditions during snow fall and snow melt, lack of high quality ground truth for evaluation of the satellite based estimates, as well as scale issues between the ground observations and the satellite footprint. In this study we utilize observations of high spatial resolution (5-km) as available from the Moderate Resolution Imaging Spectro-radiometer (MODIS) to derive surface shortwave radiative fluxes in complex terrain, with attention to the impact of slopes on the amount of radiation received. The methodology developed has been applied to several water years (January to July during 2003, 2004, 2005 and 2009) over the western part of the United States, and the available information was used to derive metrics on spatial and temporal variability in the shortwave fluxes. It is planned to apply the findings from this study for testing improvements in Snow Water Equivalent (SWE) estimates.

A Randomized Trial of an Elementary School Mathematics Software Intervention: Spatial-Temporal Math

Science.gov (United States)

Rutherford, Teomara; Farkas, George; Duncan, Greg; Burchinal, Margaret; Kibrick, Melissa; Graham, Jeneen; Richland, Lindsey; Tran, Natalie; Schneider, Stephanie; Duran, Lauren; Martinez, Michael E.

2014-01-01

Fifty-two low performing schools were randomly assigned to receive Spatial-Temporal (ST) Math, a supplemental mathematics software and instructional program, in second/third or fourth/fifth grades or to a business-as-usual control. Analyses reveal a negligible effect of ST Math on mathematics scores, which did not differ significantly across…

Prediction of spatially variable unsaturated hydraulic conductivity using scaled particle-size distribution functions

NARCIS (Netherlands)

Nasta, P.; Romano, N.; Assouline, S; Vrugt, J.A.; Hopmans, J.W.

2013-01-01

Simultaneous scaling of soil water retention and hydraulic conductivity functions provides an effective means to characterize the heterogeneity and spatial variability of soil hydraulic properties in a given study area. The statistical significance of this approach largely depends on the number of

Spatial Variability and Geostatistical Prediction of Some Soil Hydraulic Coefficients of a Calcareous Soil

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Ali Akbar Moosavi

2017-02-01

calculated in various directions and their surface semivariograms were also prepared to determine the isotropic or anisotropic behavior of each studied soil attributes. Since all of studied soil hydraulic attributes were isotropic variables, therefore, the omnidirectional semivariograms were calculated and different theoretical models were fitted to them. The best fitted semivariogram models were determined using the determination coefficient, R2, and the residual sum of the square, RSS. The parameters of the best fitted models to the experimental semivariograms were also determined. The prediction of study hydraulic attributes was carried out using the parameters of semivariogram models by applying the ordinary Kriging approach. Predictions were also carried out using the Inverse Distance Weighing approach. The results of predictions were compared to each other using the Jackknifing evaluation approach and the suitable prediction method was determined and zoning was performed using the results of introducing prediction method. All of the semivariogram calculations and modeling, prediction of zoning of study hydraulic attributes were performed using the GS+ 5.1 software packages. Results and Discussion: Results indicated that all of the studied soil hydraulic attributes belonged to the weak to moderated spatial correlation classes and the spherical model was the best fitted model for their semivariograms (except for Kfs and D that their best semivariogram models were exponential. The sill of all semivariograms ranged between 0.0003 to 0.419 for the S and Kfs, respectively. The nugget effects and the Range parameter of all semivariograms were located between 0.00015 to 0.108 for the S and Фm, and 211 to 6.4 m for Ks and D, respectively. Results also indicated that 3.5 and 50% of total variation of D and Ks was spatially structured and the other was random, respectively. The spatial correlation classes of near saturated soil hydraulic conductivity and soil hydraulic

Modeling Spatial and Temporal Variability in Ammonia Emissions from Agricultural Fertilization

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Balasubramanian, S.; Koloutsou-Vakakis, S.; Rood, M. J.

2013-12-01

Ammonia (NH3), is an important component of the reactive nitrogen cycle and a precursor to formation of atmospheric particulate matter (PM). Predicting regional PM concentrations and deposition of nitrogen species to ecosystems requires representative emission inventories. Emission inventories have traditionally been developed using top down approaches and more recently from data assimilation based on satellite and ground based ambient concentrations and wet deposition data. The National Emission Inventory (NEI) indicates agricultural fertilization as the predominant contributor (56%) to NH3 emissions in Midwest USA, in 2002. However, due to limited understanding of the complex interactions between fertilizer usage, farm practices, soil and meteorological conditions and absence of detailed statistical data, such emission estimates are currently based on generic emission factors, time-averaged temporal factors and coarse spatial resolution. Given the significance of this source, our study focuses on developing an improved NH3 emission inventory for agricultural fertilization at finer spatial and temporal scales for air quality modeling studies. Firstly, a high-spatial resolution 4 km x 4 km NH3 emission inventory for agricultural fertilization has been developed for Illinois by modifying spatial allocation of emissions based on combining crop-specific fertilization rates with cropland distribution in the Sparse Matrix Operator Kernel Emissions model. Net emission estimates of our method are within 2% of NEI, since both methods are constrained by fertilizer sales data. However, we identified localized crop-specific NH3 emission hotspots at sub-county resolutions absent in NEI. Secondly, we have adopted the use of the DeNitrification-DeComposition (DNDC) Biogeochemistry model to simulate the physical and chemical processes that control volatilization of nitrogen as NH3 to the atmosphere after fertilizer application and resolve the variability at the hourly scale

Summer temperature and spatial variability of all-cause mortality in Surat city, India

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S K Rathi

2017-01-01

Full Text Available Background: Ample information is available on extreme heat associated mortality for few Indian cities, but scant literature is available on effect of temperature on spatial variability of all-cause mortality for coastal cities. Objective: To assess the effect of daily maximum temperature, relative humidity and heat index on spatial variability of all-cause mortality for summer months (March to May from 2014 to 2015 for the urban population of Surat (coastal city. Materials and Methods: Retrospective analysis of the all-cause mortality data with temperature and humidity was performed on a total of 9,237 deaths for 184 summer days (2014-2015. Climatic and all-cause mortality data were obtained through Tutiempo website and Surat Municipal Corporation respectively. Bivariate analysis performed through SPSS. Observations: Mean daily mortality was estimated at 50.2 ± 8.5 for the study period with a rise of 20% all-cause mortality at temperature ≥ 40°C and rise of 10% deaths per day during extreme danger level (HI: > 54°C days. Spatial (Zone wise analysis revealed rise of 61% all-cause mortality for Southeast and 30% for East zones at temperature ≥ 40°C. Conclusions: All-cause mortality increased on high summer temperature days. Presence of spatial variation in all-cause mortality provided the evidence for high risk zones. Findings may be helpful in designing the interventions at micro level.

Variability of apparently homogeneous soilscapes in São Paulo state, Brazil: I. spatial analysis

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M. van Den Berg

2000-06-01

Full Text Available The spatial variability of strongly weathered soils under sugarcane and soybean/wheat rotation was quantitatively assessed on 33 fields in two regions in São Paulo State, Brazil: Araras (15 fields with sugarcane and Assis (11 fields with sugarcane and seven fields with soybean/wheat rotation. Statistical methods used were: nested analysis of variance (for 11 fields, semivariance analysis and analysis of variance within and between fields. Spatial levels from 50 m to several km were analyzed. Results are discussed with reference to a previously published study carried out in the surroundings of Passo Fundo (RS. Similar variability patterns were found for clay content, organic C content and cation exchange capacity. The fields studied are quite homogeneous with respect to these relatively stable soil characteristics. Spatial variability of other characteristics (resin extractable P, pH, base- and Al-saturation and also soil colour, varies with region and, or land use management. Soil management for sugarcane seems to have induced modifications to greater depths than for soybean/wheat rotation. Surface layers of soils under soybean/wheat present relatively little variation, apparently as a result of very intensive soil management. The major part of within-field variation occurs at short distances (< 50 m in all study areas. Hence, little extra information would be gained by increasing sampling density from, say, 1/km² to 1/50 m². For many purposes, the soils in the study regions can be mapped with the same observation density, but residual variance will not be the same in all areas. Bulk sampling may help to reveal spatial patterns between 50 and 1.000 m.

Spatial variability of turbulent fluxes in the roughness sublayer of an even-aged pine forest

Science.gov (United States)

Katul, G.; Hsieh, C.-I.; Bowling, D.; Clark, K.; Shurpali, N.; Turnipseed, A.; Albertson, J.; Tu, K.; Hollinger, D.; Evans, B. M.; Offerle, B.; Anderson, D.; Ellsworth, D.; Vogel, C.; Oren, R.

1999-01-01

The spatial variability of turbulent flow statistics in the roughness sublayer (RSL) of a uniform even-aged 14 m (= h) tall loblolly pine forest was investigated experimentally. Using seven existing walkup towers at this stand, high frequency velocity, temperature, water vapour and carbon dioxide concentrations were measured at 15.5 m above the ground surface from October 6 to 10 in 1997. These seven towers were separated by at least 100 m from each other. The objective of this study was to examine whether single tower turbulence statistics measurements represent the flow properties of RSL turbulence above a uniform even-aged managed loblolly pine forest as a best-case scenario for natural forested ecosystems. From the intensive space-time series measurements, it was demonstrated that standard deviations of longitudinal and vertical velocities (??(u), ??(w)) and temperature (??(T)) are more planar homogeneous than their vertical flux of momentum (u(*)2) and sensible heat (H) counterparts. Also, the measured H is more horizontally homogeneous when compared to fluxes of other scalar entities such as CO2 and water vapour. While the spatial variability in fluxes was significant (> 15%), this unique data set confirmed that single tower measurements represent the 'canonical' structure of single-point RSL turbulence statistics, especially flux-variance relationships. Implications to extending the 'moving-equilibrium' hypothesis for RSL flows are discussed. The spatial variability in all RSL flow variables was not constant in time and varied strongly with spatially averaged friction velocity u(*), especially when u(*) was small. It is shown that flow properties derived from two-point temporal statistics such as correlation functions are more sensitive to local variability in leaf area density when compared to single point flow statistics. Specifically, that the local relationship between the reciprocal of the vertical velocity integral time scale (I(w)) and the arrival

Spatial Variability Analysis of Within-Field Winter Wheat Nitrogen and Grain Quality Using Canopy Fluorescence Sensor Measurements

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Xiaoyu Song

2017-03-01

Full Text Available Wheat grain protein content (GPC is a key component when evaluating wheat nutrition. It is also important to determine wheat GPC before harvest for agricultural and food process enterprises in order to optimize the wheat grading process. Wheat GPC across a field is spatially variable due to the inherent variability of soil properties and position in the landscape. The objectives of this field study were: (i to assess the spatial and temporal variability of wheat nitrogen (N attributes related to the grain quality of winter wheat production through canopy fluorescence sensor measurements; and (ii to examine the influence of spatial variability of soil N and moisture across different growth stages on the wheat grain quality. A geostatistical approach was used to analyze data collected from 110 georeferenced locations. In particular, Ordinary Kriging Analysis (OKA was used to produce maps of wheat GPC, GPC yield, and wheat canopy fluorescence parameters, including simple florescence ratio and Nitrogen Balance Indices (NBI. Soil Nitrate-Nitrogen (NO3-N content and soil Time Domain Reflectometry (TDR value in the study field were also interpolated through the OKA method. The fluorescence parameter maps, soil NO3-N and soil TDR maps obtained from the OKA output were compared with the wheat GPC and GPC yield maps in order to assess their relationships. The results of this study indicate that the NBI spatial variability map in the late stage of wheat growth can be used to distinguish areas that produce higher GPC.

Groundwater Quality: Analysis of Its Temporal and Spatial Variability in a Karst Aquifer.

Science.gov (United States)

Pacheco Castro, Roger; Pacheco Ávila, Julia; Ye, Ming; Cabrera Sansores, Armando

2018-01-01

This study develops an approach based on hierarchical cluster analysis for investigating the spatial and temporal variation of water quality governing processes. The water quality data used in this study were collected in the karst aquifer of Yucatan, Mexico, the only source of drinking water for a population of nearly two million people. Hierarchical cluster analysis was applied to the quality data of all the sampling periods lumped together. This was motivated by the observation that, if water quality does not vary significantly in time, two samples from the same sampling site will belong to the same cluster. The resulting distribution maps of clusters and box-plots of the major chemical components reveal the spatial and temporal variability of groundwater quality. Principal component analysis was used to verify the results of cluster analysis and to derive the variables that explained most of the variation of the groundwater quality data. Results of this work increase the knowledge about how precipitation and human contamination impact groundwater quality in Yucatan. Spatial variability of groundwater quality in the study area is caused by: a) seawater intrusion and groundwater rich in sulfates at the west and in the coast, b) water rock interactions and the average annual precipitation at the middle and east zones respectively, and c) human contamination present in two localized zones. Changes in the amount and distribution of precipitation cause temporal variation by diluting groundwater in the aquifer. This approach allows to analyze the variation of groundwater quality controlling processes efficiently and simultaneously. © 2017, National Ground Water Association.

Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.

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Guo, Yan; Huang, Jingyi; Shi, Zhou; Li, Hongyi

2015-01-01

In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9) allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v) as well as other EMI instruments (e.g. DUALEM-421) can be incorporated to conduct Quasi-3D inversions for deeper soil profiles.

Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.

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Yan Guo

Full Text Available In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9 allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v as well as other EMI instruments (e.g. DUALEM-421 can be incorporated to conduct Quasi-3D inversions for deeper soil profiles.

Mapping Spatial Variability of Soil Salinity in a Coastal Paddy Field Based on Electromagnetic Sensors

Science.gov (United States)

Guo, Yan; Huang, Jingyi; Shi, Zhou; Li, Hongyi

2015-01-01

In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9) allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v) as well as other EMI instruments (e.g. DUALEM-421) can be incorporated to conduct Quasi-3D inversions for deeper soil profiles. PMID:26020969

Using geomorphological variables to predict the spatial distribution of plant species in agricultural drainage networks.

Science.gov (United States)

Rudi, Gabrielle; Bailly, Jean-Stéphane; Vinatier, Fabrice

2018-01-01

To optimize ecosystem services provided by agricultural drainage networks (ditches) in headwater catchments, we need to manage the spatial distribution of plant species living in these networks. Geomorphological variables have been shown to be important predictors of plant distribution in other ecosystems because they control the water regime, the sediment deposition rates and the sun exposure in the ditches. Whether such variables may be used to predict plant distribution in agricultural drainage networks is unknown. We collected presence and absence data for 10 herbaceous plant species in a subset of a network of drainage ditches (35 km long) within a Mediterranean agricultural catchment. We simulated their spatial distribution with GLM and Maxent model using geomorphological variables and distance to natural lands and roads. Models were validated using k-fold cross-validation. We then compared the mean Area Under the Curve (AUC) values obtained for each model and other metrics issued from the confusion matrices between observed and predicted variables. Based on the results of all metrics, the models were efficient at predicting the distribution of seven species out of ten, confirming the relevance of geomorphological variables and distance to natural lands and roads to explain the occurrence of plant species in this Mediterranean catchment. In particular, the importance of the landscape geomorphological variables, ie the importance of the geomorphological features encompassing a broad environment around the ditch, has been highlighted. This suggests that agro-ecological measures for managing ecosystem services provided by ditch plants should focus on the control of the hydrological and sedimentological connectivity at the catchment scale. For example, the density of the ditch network could be modified or the spatial distribution of vegetative filter strips used for sediment trapping could be optimized. In addition, the vegetative filter strips could constitute

Mapping Spatial Distribution of Larch Plantations from Multi-Seasonal Landsat-8 OLI Imagery and Multi-Scale Textures Using Random Forests

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Tian Gao

2015-02-01

Full Text Available The knowledge about spatial distribution of plantation forests is critical for forest management, monitoring programs and functional assessment. This study demonstrates the potential of multi-seasonal (spring, summer, autumn and winter Landsat-8 Operational Land Imager imageries with random forests (RF modeling to map larch plantations (LP in a typical plantation forest landscape in North China. The spectral bands and two types of textures were applied for creating 675 input variables of RF. An accuracy of 92.7% for LP, with a Kappa coefficient of 0.834, was attained using the RF model. A RF-based importance assessment reveals that the spectral bands and bivariate textural features calculated by pseudo-cross variogram (PC strongly promoted forest class-separability, whereas the univariate textural features influenced weakly. A feature selection strategy eliminated 93% of variables, and then a subset of the 47 most essential variables was generated. In this subset, PC texture derived from summer and winter appeared the most frequently, suggesting that this variability in growing peak season and non-growing season can effectively enhance forest class-separability. A RF classifier applied to the subset led to 91.9% accuracy for LP, with a Kappa coefficient of 0.829. This study provides an insight into approaches for discriminating plantation forests with phenological behaviors.

A hydrochemical modelling framework for combined assessment of spatial and temporal variability in stream chemistry: application to Plynlimon, Wales

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H.J. Foster

2001-01-01

Full Text Available Recent concern about the risk to biota from acidification in upland areas, due to air pollution and land-use change (such as the planting of coniferous forests, has generated a need to model catchment hydro-chemistry to assess environmental risk and define protection strategies. Previous approaches have tended to concentrate on quantifying either spatial variability at a regional scale or temporal variability at a given location. However, to protect biota from ‘acid episodes’, an assessment of both temporal and spatial variability of stream chemistry is required at a catchment scale. In addition, quantification of temporal variability needs to represent both episodic event response and long term variability caused by deposition and/or land-use change. Both spatial and temporal variability in streamwater chemistry are considered in a new modelling methodology based on application to the Plynlimon catchments, central Wales. A two-component End-Member Mixing Analysis (EMMA is used whereby low and high flow chemistry are taken to represent ‘groundwater’ and ‘soil water’ end-members. The conventional EMMA method is extended to incorporate spatial variability in the two end-members across the catchments by quantifying the Acid Neutralisation Capacity (ANC of each in terms of a statistical distribution. These are then input as stochastic variables to a two-component mixing model, thereby accounting for variability of ANC both spatially and temporally. The model is coupled to a long-term acidification model (MAGIC to predict the evolution of the end members and, hence, the response to future scenarios. The results can be plotted as a function of time and space, which enables better assessment of the likely effects of pollution deposition or land-use changes in the future on the stream chemistry than current methods which use catchment average values. The model is also a useful basis for further research into linkage between hydrochemistry

Contribution of geodiversity, climate and spatial variables for biodiversity across a gradient of human influence

Science.gov (United States)

Tukiainen, Helena; Alahuhta, Janne; Ala-Hulkko, Terhi; Field, Richard; Lampinen, Raino; Hjort, Jan

2016-04-01

Implementation of geodiversity may provide new perspectives for nature conservation. The relation between geodiversity and biodiversity has been established in recent studies but remains underexplored in environments with high human pressure. In this study, we explored the effect of geodiversity (i.e. geological, hydrological and geomorphological diversity), climate and spatial variables on biodiversity (vascular plant species richness) in environments with different human impact. The study area ranged trough the boreal vegetation zone in Finland and included altogether 1401 1-km2 grid cells from urban, rural and natural environments. The contribution of environmental variable groups for species diversity in different environments was statistically analyzed with variation partitioning method. According to the results, the contribution of geodiversity decreased and the contribution of climate and spatial variables increased as the land use became more human-induced. Hence, the connection between geodiversity and species richness was most pronounced in natural state environments.

Longterm and spatial variability of Aerosol optical properties measured by sky radiometer in Japan sites

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Aoki, K.

2016-12-01

Aerosols and cloud play an important role in the climate change. We started the long-term monitoring of aerosol and cloud optical properties since 1990's by using sky radiometer (POM-01, 02; Prede Co. Ltd., Japan). We provide the information, in this presentation, on the aerosol optical properties with respect to their temporal and spatial variability in Japan site (ex. Sapporo, Toyama, Kasuga and etc). The global distributions of aerosols have been derived from earth observation satellite and have been simulated in numerical models, which assume optical parameters. However, these distributions are difficult to derive because of variability in time and space. Therefore, Aerosol optical properties were investigated using the measurements from ground-based and ship-borne sky radiometer. The sky radiometer is an automatic instrument that takes observations only in daytime under the clear sky conditions. Observation of diffuse solar intensity interval was made every ten or five minutes by once. The aerosol optical properties were computed using the SKYRAD.pack version 4.2. The obtained Aerosol optical properties (Aerosol optical thickness, Ångström exponent, Single scattering albedo, and etc.) and size distribution volume clearly showed spatial and temporal variability in Japan area. In this study, we present the temporal and spatial variability of Aerosol optical properties at several Japan sites, applied to validation of satellite and numerical models. This project is validation satellite of GCOM-C, JAXA. The GCOM-C satellite scheduled to be launched in early 2017.

Prediction of the spatial occurrence of fire induced spalling in concrete slabs using random fields

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Van Coile R.

2013-09-01

Full Text Available As the loss of concrete cover can significantly influence the reliability of concrete elements during fire, spalling should be taken into account when performing reliability calculations. However, the occurrence and spatial variation of spalling are highly uncertain. A first step towards a probabilistic analysis of spalling is made by combining existing deterministic models with a stochastic representation of the concrete tensile strength and by using random fields to model the tensile strength spatial variation.

Precision Viticulture : is it relevant to manage the vineyard according to the within field spatial variability of the environment ?

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Tisseyre, Bruno

2015-04-01

For more than 15 years, research projects are conducted in the precision viticulture (PV) area around the world. These research projects have provided new insights into the within-field variability in viticulture. Indeed, access to high spatial resolution data (remote sensing, embedded sensors, etc.) changes the knowledge we have of the fields in viticulture. In particular, the field which was until now considered as a homogeneous management unit, presents actually a high spatial variability in terms of yield, vigour an quality. This knowledge will lead (and is already causing) changes on how to manage the vineyard and the quality of the harvest at the within field scale. From the experimental results obtained in various countries of the world, the goal of the presentation is to provide figures on: - the spatial variability of the main parameters (yield, vigor, quality), and how this variability is organized spatially, - the temporal stability of the observed spatial variability and the potential link with environmental parameters like soil, topography, soil water availability, etc. - information sources available at a high spatial resolution conventionally used in precision agriculture likely to highlight this spatial variability (multi-spectral images, soil electrical conductivity, etc.) and the limitations that these information sources are likely to present in viticulture. Several strategies are currently being developed to take into account the within field variability in viticulture. They are based on the development of specific equipments, sensors, actuators and site specific strategies with the aim of adapting the vineyard operations at the within-field level. These strategies will be presented briefly in two ways : - Site specific operations (fertilization, pruning, thinning, irrigation, etc.) in order to counteract the effects of the environment and to obtain a final product with a controlled and consistent wine quality, - Differential harvesting with the

Alaskan soil carbon stocks: spatial variability and dependence on environmental factors

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U. Mishra

2012-09-01

Full Text Available The direction and magnitude of soil organic carbon (SOC changes in response to climate change depend on the spatial and vertical distributions of SOC. We estimated spatially resolved SOC stocks from surface to C horizon, distinguishing active-layer and permafrost-layer stocks, based on geospatial analysis of 472 soil profiles and spatially referenced environmental variables for Alaska. Total Alaska state-wide SOC stock was estimated to be 77 Pg, with 61% in the active-layer, 27% in permafrost, and 12% in non-permafrost soils. Prediction accuracy was highest for the active-layer as demonstrated by highest ratio of performance to deviation (1.5. Large spatial variability was predicted, with whole-profile, active-layer, and permafrost-layer stocks ranging from 1–296 kg C m⁻², 2–166 kg m⁻², and 0–232 kg m⁻², respectively. Temperature and soil wetness were found to be primary controllers of whole-profile, active-layer, and permafrost-layer SOC stocks. Secondary controllers, in order of importance, were found to be land cover type, topographic attributes, and bedrock geology. The observed importance of soil wetness rather than precipitation on SOC stocks implies that the poor representation of high-latitude soil wetness in Earth system models may lead to large uncertainty in predicted SOC stocks under future climate change scenarios. Under strict caveats described in the text and assuming temperature changes from the A1B Intergovernmental Panel on Climate Change emissions scenario, our geospatial model indicates that the equilibrium average 2100 Alaska active-layer depth could deepen by 11 cm, resulting in a thawing of 13 Pg C currently in permafrost. The equilibrium SOC loss associated with this warming would be highest under continuous permafrost (31%, followed by discontinuous (28%, isolated (24.3%, and sporadic (23.6% permafrost areas. Our high-resolution mapping of soil carbon stock reveals the

Temporal and spatial variability of wind resources in the United States as derived from the Climate Forecast System Reanalysis

Science.gov (United States)

Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman

2015-01-01

This study examines the spatial and temporal variability of wind speed at 80m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the...

Discrete curved ray-tracing method for radiative transfer in an absorbing-emitting semitransparent slab with variable spatial refractive index

International Nuclear Information System (INIS)

Liu, L.H.

2004-01-01

A discrete curved ray-tracing method is developed to analyze the radiative transfer in one-dimensional absorbing-emitting semitransparent slab with variable spatial refractive index. The curved ray trajectory is locally treated as straight line and the complicated and time-consuming computation of ray trajectory is cut down. A problem of radiative equilibrium with linear variable spatial refractive index is taken as an example to examine the accuracy of the proposed method. The temperature distributions are determined by the proposed method and compared with the data in references, which are obtained by other different methods. The results show that the discrete curved ray-tracing method has a good accuracy in solving the radiative transfer in one-dimensional semitransparent slab with variable spatial refractive index

Spatial variability of soil carbon across Mexico and the United States

Science.gov (United States)

Vargas, R.; Guevara, M.; Cruz Gaistardo, C.; Paz, F.; de Jong, B.; Etchevers, J.

2015-12-01

Soil organic carbon (SOC) is directly linked to soil quality, food security, and land use/global environmental change. We use publicly available information on SOC and couple it with digital elevation models and derived terrain attributes using a machine learning approach. We found a strong spatial dependency of SOC across the United States, but less spatial dependency of SOC across Mexico. Using High Performance Computing (HPC) we derived a 1 km resolution map of SOC across Mexico and the United States. We tested different machine learning methods (e.g., kernel based, tree based and/or Geo-statistics approaches) for computational efficiency and statistical accuracy. Using random forest combined with geo-statistics we were able to explain >70% of SOC variance for Mexico and >40% in the case of the United States via cross validation. These results compare with other published estimates of SOC at 1km resolution that only explain <30% of SOC variance across the world. Topographic attributes derived from digital elevation models are freely available globally at fine spatial resolution (<100 m), and this information allowed us to make predictions of SOC at fine scales. We further tested this approach using SOC information from the International Soil Carbon Network to predict SOC in other regions of the world. We conclude that this approach (using public information and open source platforms for data analysis) could be implemented to predict detailed explicit information of SOC across different spatial scales.

Controls of Soil Spatial Variability in a Dry Tropical Forest.

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Sandeep Pulla

Full Text Available We examined the roles of lithology, topography, vegetation and fire in generating local-scale (<1 km2 soil spatial variability in a seasonally dry tropical forest (SDTF in southern India. For this, we mapped soil (available nutrients, Al, total C, pH, moisture and texture in the top 10 cm, rock outcrops, topography, all native woody plants ≥1 cm diameter at breast height (DBH, and spatial variation in fire frequency (times burnt during the 17 years preceding soil sampling in a permanent 50-ha plot. Unlike classic catenas, lower elevation soils had lesser moisture, plant-available Ca, Cu, Mn, Mg, Zn, B, clay and total C. The distribution of plant-available Ca, Cu, Mn and Mg appeared to largely be determined by the whole-rock chemical composition differences between amphibolites and hornblende-biotite gneisses. Amphibolites were associated with summit positions, while gneisses dominated lower elevations, an observation that concurs with other studies in the region which suggest that hillslope-scale topography has been shaped by differential weathering of lithologies. Neither NO3(--N nor NH4(+-N was explained by the basal area of trees belonging to Fabaceae, a family associated with N-fixing species, and no long-term effects of fire on soil parameters were detected. Local-scale lithological variation is an important first-order control over soil variability at the hillslope scale in this SDTF, by both direct influence on nutrient stocks and indirect influence via control of local relief.

Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure

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Mabaso Musawenkosi LH

2007-09-01

Full Text Available Abstract Background Several malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana. Results Of 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country. Conclusion We have

Spatial and temporal variability of groundwater recharge in Geba basin, Northern Ethiopia

Science.gov (United States)

Yenehun, Alemu; Walraevens, Kristine; Batelaan, Okke

2017-10-01

WetSpa, a physically based, spatially distributed watershed model, has been used to study the spatial and temporal variation of recharge in the Geba basin, Northern Ethiopia. The model covers an area of about 4, 249 km2 and integrates elevation, soil and land-use data, hydrometeorological and river discharge data. The Geba basin has a highly variable topography ranging from 1000 to 3280 m with an average slope of 12.9%. The area is characterized by a distinct wet and long dry season with a mean annual precipitation of 681 mm and temperatures ranging between 6.5 °C and 32 °C. The model was simulated on daily basis for nearly four years (January 1, 2000 to December 18, 2003). It resulted in a good agreement between measured and simulated streamflow hydrographs with Nash-Sutcliffe efficiency of almost 70% and 85% for, respectively, the calibration and validation. The water balance terms show very strong spatial and temporal variability, about 3.8% of the total precipitation is intercepted by the plant canopy; 87.5% infiltrates into the soil (of which 13% percolates, 2.7% flows laterally off and 84.2% evapotranspired from the root zone), and 7.2% is surface runoff. The mean annual recharge varies from about 45 mm (2003) to 208 mm (2001), with average of 98.6 mm/yr. On monthly basis, August has the maximum (73 mm) and December the lowest (0.1 mm) recharge. The mean annual groundwater recharge spatially varies from 0 to 371 mm; mainly controlled by the distribution of rainfall amount, followed by soil and land-use, and to a certain extent, slope. About 21% of Geba has a recharge larger than 120 mm and 1% less than 5 mm.

Statistical Analysis for Multisite Trials Using Instrumental Variables with Random Coefficients

Science.gov (United States)

Raudenbush, Stephen W.; Reardon, Sean F.; Nomi, Takako

2012-01-01

Multisite trials can clarify the average impact of a new program and the heterogeneity of impacts across sites. Unfortunately, in many applications, compliance with treatment assignment is imperfect. For these applications, we propose an instrumental variable (IV) model with person-specific and site-specific random coefficients. Site-specific IV…

ASSESSMENT SPATIAL VARIABILITY OF SOIL ERODIBILITY BY USING OF GEOSTATISTIC AND GIS (Case study MEHR watershed of SABZEVAR

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Ayoubi, S.A

2005-05-01

Full Text Available Soil erodibility is one of the key factors on some sediment and soil erosion models such as USLE, MUSLE, RUSLE, AUSLE (USLE modified in LS factor and MMF and represents like K factor and is function of particle distribution, organic mater, soil structure and ermeability. Traditional methods do not take spatial variability and estimate precision of variables in to consideration and amount of them are constant across the whole of soil series .This study was performed to assess spatial variability of soil erodibility and its relevant variables at MEHR watershed from Khorasan province, in northern Iran. Interested network was designed by 110 samples like nested- systematic with distance about 50, 100, 250 and 500 meter across the study area by preparing point map at GIS. Sampling points were identified in field by an Global Positioning system. Soil sampling was done at depth of 0-5cm of ground surface and permeability was studied at depth of 5-30 cm. Some soil properties such as particle distribution and organic mater were measured at laboratory. Particle size distribution was determined by Hydrometer method and Organic matter was measured by wet oxidation approach. Then spatial analysis was done. Variography analysis on soil attributes according to soil erodibility, showed that Gaussian, exponential and spherical models were the most models to predict spatial variability of soil parameters. The range of spatial dependencies was changed from 320 to 3200 m. Soil attribute maps prepared by kriging technique using models parameters. Then soil attributes were composed by Wischmeier (1978 formula in Illwis media to calculate K factor. Amount of soil erodibility changed from 0.13 to 0.91 that it's maximum and minimum was identified in east and southwest of studiedarea. Soil spatial variability pattern, is similar to silt pattern due to high effect of silt on soil rodibility, Also that is partially confirmed with geology map, indicated which soil

Disturbance History,Spatial Variability, and Patterns of Biodiversity

Science.gov (United States)

Bendix, J.; Wiley, J. J.; Commons, M.

2012-12-01

The intermediate disturbance hypothesis predicts that species diversity will be maximized in environments experiencing intermediate intensity disturbance, after an intermediate timespan. Because many landscapes comprise mosaics with complex disturbance histories, the theory implies that each patch in those mosaics should have a distinct level of diversity reflecting combined impact of the magnitude of disturbance and the time since it occurred. We modeled the changing patterns of species richness across a landscape experiencing varied scenarios of simulated disturbance. Model outputs show that individual landscape patches have highly variable species richness through time, with the details reflecting the timing, intensity and sequence of their disturbance history. When the results are mapped across the landscape, the resulting temporal and spatial complexity illustrates both the contingent nature of diversity and the danger of generalizing about the impacts of disturbance.

Multilevel discretized random field models with 'spin' correlations for the simulation of environmental spatial data

Science.gov (United States)

Žukovič, Milan; Hristopulos, Dionissios T.

2009-02-01

A current problem of practical significance is how to analyze large, spatially distributed, environmental data sets. The problem is more challenging for variables that follow non-Gaussian distributions. We show by means of numerical simulations that the spatial correlations between variables can be captured by interactions between 'spins'. The spins represent multilevel discretizations of environmental variables with respect to a number of pre-defined thresholds. The spatial dependence between the 'spins' is imposed by means of short-range interactions. We present two approaches, inspired by the Ising and Potts models, that generate conditional simulations of spatially distributed variables from samples with missing data. Currently, the sampling and simulation points are assumed to be at the nodes of a regular grid. The conditional simulations of the 'spin system' are forced to respect locally the sample values and the system statistics globally. The second constraint is enforced by minimizing a cost function representing the deviation between normalized correlation energies of the simulated and the sample distributions. In the approach based on the Nc-state Potts model, each point is assigned to one of Nc classes. The interactions involve all the points simultaneously. In the Ising model approach, a sequential simulation scheme is used: the discretization at each simulation level is binomial (i.e., ± 1). Information propagates from lower to higher levels as the simulation proceeds. We compare the two approaches in terms of their ability to reproduce the target statistics (e.g., the histogram and the variogram of the sample distribution), to predict data at unsampled locations, as well as in terms of their computational complexity. The comparison is based on a non-Gaussian data set (derived from a digital elevation model of the Walker Lake area, Nevada, USA). We discuss the impact of relevant simulation parameters, such as the domain size, the number of

Estimation of Poisson noise in spatial domain

Science.gov (United States)

Švihlík, Jan; Fliegel, Karel; Vítek, Stanislav; Kukal, Jaromír.; Krbcová, Zuzana

2017-09-01

This paper deals with modeling of astronomical images in the spatial domain. We consider astronomical light images contaminated by the dark current which is modeled by Poisson random process. Dark frame image maps the thermally generated charge of the CCD sensor. In this paper, we solve the problem of an addition of two Poisson random variables. At first, the noise analysis of images obtained from the astronomical camera is performed. It allows estimating parameters of the Poisson probability mass functions in every pixel of the acquired dark frame. Then the resulting distributions of the light image can be found. If the distributions of the light image pixels are identified, then the denoising algorithm can be applied. The performance of the Bayesian approach in the spatial domain is compared with the direct approach based on the method of moments and the dark frame subtraction.

Sums and Products of Jointly Distributed Random Variables: A Simplified Approach

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Stein, Sheldon H.

2005-01-01

Three basic theorems concerning expected values and variances of sums and products of random variables play an important role in mathematical statistics and its applications in education, business, the social sciences, and the natural sciences. A solid understanding of these theorems requires that students be familiar with the proofs of these…

Central limit theorem for the Banach-valued weakly dependent random variables

International Nuclear Information System (INIS)

Dmitrovskij, V.A.; Ermakov, S.V.; Ostrovskij, E.I.

1983-01-01

The central limit theorem (CLT) for the Banach-valued weakly dependent random variables is proved. In proving CLT convergence of finite-measured (i.e. cylindrical) distributions is established. A weak compactness of the family of measures generated by a certain sequence is confirmed. The continuity of the limiting field is checked

Free random variables

CERN Document Server

Voiculescu, Dan; Nica, Alexandru

1992-01-01

This book presents the first comprehensive introduction to free probability theory, a highly noncommutative probability theory with independence based on free products instead of tensor products. Basic examples of this kind of theory are provided by convolution operators on free groups and by the asymptotic behavior of large Gaussian random matrices. The probabilistic approach to free products has led to a recent surge of new results on the von Neumann algebras of free groups. The book is ideally suited as a textbook for an advanced graduate course and could also provide material for a seminar. In addition to researchers and graduate students in mathematics, this book will be of interest to physicists and others who use random matrices.

Spatial and temporal analysis of drought variability at several time scales in Syria during 1961-2012

Science.gov (United States)

Mathbout, Shifa; Lopez-Bustins, Joan A.; Martin-Vide, Javier; Bech, Joan; Rodrigo, Fernando S.

2018-02-01

This paper analyses the observed spatiotemporal characteristics of drought phenomenon in Syria using the Standardised Precipitation Index (SPI) and the Standardised Precipitation Evapotranspiration Index (SPEI). Temporal variability of drought is calculated for various time scales (3, 6, 9, 12, and 24 months) for 20 weather stations over the 1961-2012 period. The spatial patterns of drought were identified by applying a Principal Component Analysis (PCA) to the SPI and SPEI values at different time scales. The results revealed three heterogeneous and spatially well-defined regions with different temporal evolution of droughts: 1) Northeastern (inland desert); 2) Southern (mountainous landscape); 3) Northwestern (Mediterranean coast). The evolutionary characteristics of drought during 1961-2012 were analysed including spatial and temporal variability of SPI and SPEI, the frequency distribution, and the drought duration. The results of the non-parametric Mann-Kendall test applied to the SPI and SPEI series indicate prevailing significant negative trends (drought) at all stations. Both drought indices have been correlated both on spatial and temporal scales and they are highly comparable, especially, over a 12 and 24 month accumulation period. We concluded that the temporal and spatial characteristics of the SPI and SPEI can be used for developing a drought intensity - areal extent - and frequency curve that assesses the variability of regional droughts in Syria. The analysis of both indices suggests that all three regions had a severe drought in the 1990s, which had never been observed before in the country. Furthermore, the 2007-2010 drought was the driest period in the instrumental record, happening just before the onset of the recent conflict in Syria.

Environmental versus demographic variability in stochastic predator–prey models

International Nuclear Information System (INIS)

Dobramysl, U; Täuber, U C

2013-01-01

In contrast to the neutral population cycles of the deterministic mean-field Lotka–Volterra rate equations, including spatial structure and stochastic noise in models for predator–prey interactions yields complex spatio-temporal structures associated with long-lived erratic population oscillations. Environmental variability in the form of quenched spatial randomness in the predation rates results in more localized activity patches. Our previous study showed that population fluctuations in rare favorable regions in turn cause a remarkable increase in the asymptotic densities of both predators and prey. Very intriguing features are found when variable interaction rates are affixed to individual particles rather than lattice sites. Stochastic dynamics with demographic variability in conjunction with inheritable predation efficiencies generate non-trivial time evolution for the predation rate distributions, yet with overall essentially neutral optimization. (paper)

Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas - A review

NARCIS (Netherlands)

Cristiano, E.; ten Veldhuis, J.A.E.; van de Giesen, N.C.

2017-01-01

In urban areas, hydrological processes are characterized by high variability in space and time, making them sensitive to small-scale temporal and spatial rainfall variability. In the last decades new instruments, techniques, and methods have been developed to capture rainfall and hydrological

Groundwater variability across temporal and spatial scales in the central and northeastern U.S.

OpenAIRE

Li, B; Rodell, M; Famiglietti, JS

2015-01-01

© 2015 Elsevier B.V. Depth-to-water measurements from 181 monitoring wells in unconfined or semi-confined aquifers in nine regions of the central and northeastern U.S. were analyzed. Groundwater storage exhibited strong seasonal variations in all regions, with peaks in spring and lows in autumn, and its interannual variability was nearly unbounded, such that the impacts of droughts, floods, and excessive pumping could persist for many years. We found that the spatial variability of groundwate...

Spatial and temporal variability of mean daily wind speeds in the Czech Republic, 1961-2015

Czech Academy of Sciences Publication Activity Database

Brázdil, Rudolf; Zahradníček, Pavel; Řezníčková, Ladislava; Tolasz, R.; Štěpánek, Petr; Dobrovolný, Petr

2017-01-01

Roč. 72, č. 3 (2017), s. 197-216 ISSN 0936-577X R&D Projects: GA MŠk(CZ) LO1415; GA ČR(CZ) GA15-11805S Institutional support: RVO:67179843 Keywords : mean daily wind speed * spatial variability * temporal variability * wind stilling * Czech Republic Subject RIV: DG - Athmosphere Sciences, Meteorology OBOR OECD: Meteorology and atmospheric sciences Impact factor: 1.578, year: 2016

Spatial variability of sediment transport processes over intra‐ and subtidal timescales within a fringing coral reef system

Science.gov (United States)

Pomeroy, Andrew; Lowe, Ryan J.; Ghisalberti, Marco; Winter, Gundula; Storlazzi, Curt D.; Cuttler, Michael V. W.

2018-01-01

Sediment produced on fringing coral reefs that is transported along the bed or in suspension affects ecological reef communities as well as the morphological development of the reef, lagoon, and adjacent shoreline. This study quantified the physical process contribution and relative importance of incident waves, infragravity waves, and mean currents to the spatial and temporal variability of sediment in suspension. Estimates of bed shear stresses demonstrate that incident waves are the key driver of the SSC variability spatially (reef flat, lagoon, and channels) but cannot not fully describe the SSC variability alone. The comparatively small but statistically significant contribution to the bed shear stress by infragravity waves and currents, along with the spatial availability of sediment of a suitable size and volume, is also important. Although intra‐tidal variability in SSC occurs in the different reef zones, the majority of the variability occurs over longer slowly varying (subtidal) time scales, which is related to the arrival of large incident waves at a reef location. The predominant flow pathway, which can transport suspended sediment, consists of cross‐reef flow across the reef flat that diverges in the lagoon and returns offshore through channels. This pathway is primarily due to subtidal variations in wave‐driven flows, but can also be driven alongshore by wind stresses when the incident waves are small. Higher frequency (intra‐tidal) current variability also occur due to both tidal flows, as well as variations in the water depth that influence wave transmission across the reef and wave‐driven currents.

Predictivity strength of the spatial variability of phenanthrene sorption across two sandy loam fields

DEFF Research Database (Denmark)

Soares, Antonio; Paradelo Pérez, Marcos; Møldrup, Per

2015-01-01

Sorption is commonly suggested as the major process underlying the transport and fate of polycyclic aromatic hydrocarbons (PAHs) in soils. However, studies focusing in spatial variability at the field scale in particular are still scarce. In order to investigate the sorption of phenanthrene...

AUTOCLASSIFICATION OF THE VARIABLE 3XMM SOURCES USING THE RANDOM FOREST MACHINE LEARNING ALGORITHM

International Nuclear Information System (INIS)

Farrell, Sean A.; Murphy, Tara; Lo, Kitty K.

2015-01-01

In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ∼92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ∼95%. Manual investigation of a random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.

Analysing earthquake slip models with the spatial prediction comparison test

KAUST Repository

Zhang, L.; Mai, Paul Martin; Thingbaijam, Kiran Kumar; Razafindrakoto, H. N. T.; Genton, Marc G.

2014-01-01

Earthquake rupture models inferred from inversions of geophysical and/or geodetic data exhibit remarkable variability due to uncertainties in modelling assumptions, the use of different inversion algorithms, or variations in data selection and data processing. A robust statistical comparison of different rupture models obtained for a single earthquake is needed to quantify the intra-event variability, both for benchmark exercises and for real earthquakes. The same approach may be useful to characterize (dis-)similarities in events that are typically grouped into a common class of events (e.g. moderate-size crustal strike-slip earthquakes or tsunamigenic large subduction earthquakes). For this purpose, we examine the performance of the spatial prediction comparison test (SPCT), a statistical test developed to compare spatial (random) fields by means of a chosen loss function that describes an error relation between a 2-D field (‘model’) and a reference model. We implement and calibrate the SPCT approach for a suite of synthetic 2-D slip distributions, generated as spatial random fields with various characteristics, and then apply the method to results of a benchmark inversion exercise with known solution. We find the SPCT to be sensitive to different spatial correlations lengths, and different heterogeneity levels of the slip distributions. The SPCT approach proves to be a simple and effective tool for ranking the slip models with respect to a reference model.

Analysing earthquake slip models with the spatial prediction comparison test

KAUST Repository

Zhang, L.

2014-11-10

Earthquake rupture models inferred from inversions of geophysical and/or geodetic data exhibit remarkable variability due to uncertainties in modelling assumptions, the use of different inversion algorithms, or variations in data selection and data processing. A robust statistical comparison of different rupture models obtained for a single earthquake is needed to quantify the intra-event variability, both for benchmark exercises and for real earthquakes. The same approach may be useful to characterize (dis-)similarities in events that are typically grouped into a common class of events (e.g. moderate-size crustal strike-slip earthquakes or tsunamigenic large subduction earthquakes). For this purpose, we examine the performance of the spatial prediction comparison test (SPCT), a statistical test developed to compare spatial (random) fields by means of a chosen loss function that describes an error relation between a 2-D field (‘model’) and a reference model. We implement and calibrate the SPCT approach for a suite of synthetic 2-D slip distributions, generated as spatial random fields with various characteristics, and then apply the method to results of a benchmark inversion exercise with known solution. We find the SPCT to be sensitive to different spatial correlations lengths, and different heterogeneity levels of the slip distributions. The SPCT approach proves to be a simple and effective tool for ranking the slip models with respect to a reference model.

Spatial variability of soil pH based on GIS combined with geostatistics in Panzhihua tobacco area

International Nuclear Information System (INIS)

Du Wei; Wang Changquan; Li Bing; Li Qiquan; Du Qian; Hu Jianxin; Liu Chaoke

2012-01-01

GIS and geostatistics were utilized to study the spatial variability of soil pH in Panzhihua tobacco area. Results showed that pH values in this area ranged from 4.5 to 8.3, especially 5.5 to 6.5, and in few areas were lower than 5.0 or higher than 7.0 which can meet the need of high-quality tobacco production. The best fitting model of variogram was exponential model with the nugget/sill of soil pH in 13.61% indicating strong spatial correlation. The change process was 5.40 km and the coefficient of determination was 0.491. The spatial variability of soil pH was mainly caused by structural factors such as cane, topography and soil type. The soil pH in Panzhihua tobacco area also showed a increasing trend of northwest to southeast trend. The pH of some areas in Caochang, Gonghe and Yumen were lower, and in Dalongtan were slightly higher. (authors)

Descriptive statistics and spatial distributions of geochemical variables associated with manganese oxide-rich phases in the northern Pacific

Science.gov (United States)

Botbol, Joseph Moses; Evenden, Gerald Ian

1989-01-01

Tables, graphs, and maps are used to portray the frequency characteristics and spatial distribution of manganese oxide-rich phase geochemical data, to characterize the northern Pacific in terms of publicly available nodule geochemical data, and to develop data portrayal methods that will facilitate data analysis. Source data are a subset of the Scripps Institute of Oceanography's Sediment Data Bank. The study area is bounded by 0° N., 40° N., 120° E., and 100° W. and is arbitrarily subdivided into 14-20°x20° geographic subregions. Frequency distributions of trace metals characterized in the original raw data are graphed as ogives, and salient parameters are tabulated. All variables are transformed to enrichment values relative to median concentration within their host subregions. Scatter plots of all pairs of original variables and their enrichment transforms are provided as an aid to the interpretation of correlations between variables. Gridded spatial distributions of all variables are portrayed as gray-scale maps. The use of tables and graphs to portray frequency statistics and gray-scale maps to portray spatial distributions is an effective way to prepare for and facilitate multivariate data analysis.

Fine scale spatial variability of microbial pesticide degradation in soil: scales, controlling factors, and implications

Directory of Open Access Journals (Sweden)

Arnaud eDechesne

2014-12-01

Full Text Available Pesticide biodegradation is a soil microbial function of critical importance for modern agriculture and its environmental impact. While it was once assumed that this activity was homogeneously distributed at the field scale, mounting evidence indicates that this is rarely the case. Here, we critically examine the literature on spatial variability of pesticide biodegradation in agricultural soil. We discuss the motivations, methods, and main findings of the primary literature. We found significant diversity in the approaches used to describe and quantify spatial heterogeneity, which complicates inter-studies comparisons. However, it is clear that the presence and activity of pesticide degraders is often highly spatially variable with coefficients of variation often exceeding 50% and frequently displays nonrandom spatial patterns. A few controlling factors have tentatively been identified across pesticide classes: they include some soil characteristics (pH and some agricultural management practices (pesticide application, tillage, while other potential controlling factors have more conflicting effects depending on the site or the pesticide. Evidence demonstrating the importance of spatial heterogeneity on the fate of pesticides in soil has been difficult to obtain but modelling and experimental systems that do not include soil’s full complexity reveal that this heterogeneity must be considered to improve prediction of pesticide biodegradation rates or of leaching risks. Overall, studying the spatial heterogeneity of pesticide biodegradation is a relatively new field at the interface of agronomy, microbial ecology, and geosciences and a wealth of novel data is being collected from these different disciplinary perspectives. We make suggestions on possible avenues to take full advantage of these investigations for a better understanding and prediction of the fate of pesticides in soil.

Spatial Field Variability Mapping of Rice Crop using Clustering Technique from Space Borne Hyperspectral Data

Science.gov (United States)

Moharana, S.; Dutta, S.

2015-12-01

Precision farming refers to field-specific management of an agricultural crop at a spatial scale with an aim to get the highest achievable yield and to achieve this spatial information on field variability is essential. The difficulty in mapping of spatial variability occurring within an agriculture field can be revealed by employing spectral techniques in hyperspectral imagery rather than multispectral imagery. However an advanced algorithm needs to be developed to fully make use of the rich information content in hyperspectral data. In the present study, potential of hyperspectral data acquired from space platform was examined to map the field variation of paddy crop and its species discrimination. This high dimensional data comprising 242 spectral narrow bands with 30m ground resolution Hyperion L1R product acquired for Assam, India (30th Sept and 3rd Oct, 2014) were allowed for necessary pre-processing steps followed by geometric correction using Hyperion L1GST product. Finally an atmospherically corrected and spatially deduced image consisting of 112 band was obtained. By employing an advanced clustering algorithm, 12 different clusters of spectral waveforms of the crop were generated from six paddy fields for each images. The findings showed that, some clusters were well discriminated representing specific rice genotypes and some clusters were mixed treating as a single rice genotype. As vegetation index (VI) is the best indicator of vegetation mapping, three ratio based VI maps were also generated and unsupervised classification was performed for it. The so obtained 12 clusters of paddy crop were mapped spatially to the derived VI maps. From these findings, the existence of heterogeneity was clearly captured in one of the 6 rice plots (rice plot no. 1) while heterogeneity was observed in rest of the 5 rice plots. The degree of heterogeneous was found more in rice plot no.6 as compared to other plots. Subsequently, spatial variability of paddy field was

Detecting high spatial variability of ice shelf basal mass balance, Roi Baudouin Ice Shelf, Antarctica

Directory of Open Access Journals (Sweden)

S. Berger

2017-11-01

Full Text Available Ice shelves control the dynamic mass loss of ice sheets through buttressing and their integrity depends on the spatial variability of their basal mass balance (BMB, i.e. the difference between refreezing and melting. Here, we present an improved technique – based on satellite observations – to capture the small-scale variability in the BMB of ice shelves. As a case study, we apply the methodology to the Roi Baudouin Ice Shelf, Dronning Maud Land, East Antarctica, and derive its yearly averaged BMB at 10 m horizontal gridding. We use mass conservation in a Lagrangian framework based on high-resolution surface velocities, atmospheric-model surface mass balance and hydrostatic ice-thickness fields (derived from TanDEM-X surface elevation. Spatial derivatives are implemented using the total-variation differentiation, which preserves abrupt changes in flow velocities and their spatial gradients. Such changes may reflect a dynamic response to localized basal melting and should be included in the mass budget. Our BMB field exhibits much spatial detail and ranges from −14.7 to 8.6 m a−1 ice equivalent. Highest melt rates are found close to the grounding line where the pressure melting point is high, and the ice shelf slope is steep. The BMB field agrees well with on-site measurements from phase-sensitive radar, although independent radar profiling indicates unresolved spatial variations in firn density. We show that an elliptical surface depression (10 m deep and with an extent of 0.7 km × 1.3 km lowers by 0.5 to 1.4 m a−1, which we tentatively attribute to a transient adaptation to hydrostatic equilibrium. We find evidence for elevated melting beneath ice shelf channels (with melting being concentrated on the channel's flanks. However, farther downstream from the grounding line, the majority of ice shelf channels advect passively (i.e. no melting nor refreezing toward the ice shelf front. Although the absolute, satellite

Constructing the reduced dynamical models of interannual climate variability from spatial-distributed time series

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Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Feigin, Alexander

2016-04-01

We suggest a method for empirical forecast of climate dynamics basing on the reconstruction of reduced dynamical models in a form of random dynamical systems [1,2] derived from observational time series. The construction of proper embedding - the set of variables determining the phase space the model works in - is no doubt the most important step in such a modeling, but this task is non-trivial due to huge dimension of time series of typical climatic fields. Actually, an appropriate expansion of observational time series is needed yielding the number of principal components considered as phase variables, which are to be efficient for the construction of low-dimensional evolution operator. We emphasize two main features the reduced models should have for capturing the main dynamical properties of the system: (i) taking into account time-lagged teleconnections in the atmosphere-ocean system and (ii) reflecting the nonlinear nature of these teleconnections. In accordance to these principles, in this report we present the methodology which includes the combination of a new way for the construction of an embedding by the spatio-temporal data expansion and nonlinear model construction on the basis of artificial neural networks. The methodology is aplied to NCEP/NCAR reanalysis data including fields of sea level pressure, geopotential height, and wind speed, covering Northern Hemisphere. Its efficiency for the interannual forecast of various climate phenomena including ENSO, PDO, NAO and strong blocking event condition over the mid latitudes, is demonstrated. Also, we investigate the ability of the models to reproduce and predict the evolution of qualitative features of the dynamics, such as spectral peaks, critical transitions and statistics of extremes. This research was supported by the Government of the Russian Federation (Agreement No. 14.Z50.31.0033 with the Institute of Applied Physics RAS) [1] Y. I. Molkov, E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, "Random

How spatial and temporal rainfall variability affect runoff across basin scales: insights from field observations in the (semi-)urbanised Charlotte watershed

Science.gov (United States)

Ten Veldhuis, M. C.; Smith, J. A.; Zhou, Z.

2017-12-01

Impacts of rainfall variability on runoff response are highly scale-dependent. Sensitivity analyses based on hydrological model simulations have shown that impacts are likely to depend on combinations of storm type, basin versus storm scale, temporal versus spatial rainfall variability. So far, few of these conclusions have been confirmed on observational grounds, since high quality datasets of spatially variable rainfall and runoff over prolonged periods are rare. Here we investigate relationships between rainfall variability and runoff response based on 30 years of radar-rainfall datasets and flow measurements for 16 hydrological basins ranging from 7 to 111 km2. Basins vary not only in scale, but also in their degree of urbanisation. We investigated temporal and spatial variability characteristics of rainfall fields across a range of spatial and temporal scales to identify main drivers for variability in runoff response. We identified 3 ranges of basin size with different temporal versus spatial rainfall variability characteristics. Total rainfall volume proved to be the dominant agent determining runoff response at all basin scales, independent of their degree of urbanisation. Peak rainfall intensity and storm core volume are of secondary importance. This applies to all runoff parameters, including runoff volume, runoff peak, volume-to-peak and lag time. Position and movement of the storm with respect to the basin have a negligible influence on runoff response, with the exception of lag times in some of the larger basins. This highlights the importance of accuracy in rainfall estimation: getting the position right but the volume wrong will inevitably lead to large errors in runoff prediction. Our study helps to identify conditions where rainfall variability matters for correct estimation of the rainfall volume as well as the associated runoff response.

Mapping the Centimeter-Scale Spatial Variability of PAHs and Microbial Populations in the Rhizosphere of Two Plants.

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Amélia Bourceret

Full Text Available Rhizoremediation uses root development and exudation to favor microbial activity. Thus it can enhance polycyclic aromatic hydrocarbon (PAH biodegradation in contaminated soils. Spatial heterogeneity of rhizosphere processes, mainly linked to the root development stage and to the plant species, could explain the contrasted rhizoremediation efficiency levels reported in the literature. Aim of the present study was to test if spatial variability in the whole plant rhizosphere, explored at the centimetre-scale, would influence the abundance of microorganisms (bacteria and fungi, and the abundance and activity of PAH-degrading bacteria, leading to spatial variability in PAH concentrations. Two contrasted rhizospheres were compared after 37 days of alfalfa or ryegrass growth in independent rhizotron devices. Almost all spiked PAHs were degraded, and the density of the PAH-degrading bacterial populations increased in both rhizospheres during the incubation period. Mapping of multiparametric data through geostatistical estimation (kriging revealed that although root biomass was spatially structured, PAH distribution was not. However a greater variability of the PAH content was observed in the rhizosphere of alfalfa. Yet, in the ryegrass-planted rhizotron, the Gram-positive PAH-degraders followed a reverse depth gradient to root biomass, but were positively correlated to the soil pH and carbohydrate concentrations. The two rhizospheres structured the microbial community differently: a fungus-to-bacterium depth gradient similar to the root biomass gradient only formed in the alfalfa rhizotron.

Potential for tree rings to reveal spatial patterns of past drought variability across western Australia

Science.gov (United States)

O'Donnell, Alison J.; Cook, Edward R.; Palmer, Jonathan G.; Turney, Chris S. M.; Grierson, Pauline F.

2018-02-01

Proxy records have provided major insights into the variability of past climates over long timescales. However, for much of the Southern Hemisphere, the ability to identify spatial patterns of past climatic variability is constrained by the sparse distribution of proxy records. This is particularly true for mainland Australia, where relatively few proxy records are located. Here, we (1) assess the potential to use existing proxy records in the Australasian region—starting with the only two multi-century tree-ring proxies from mainland Australia—to reveal spatial patterns of past hydroclimatic variability across the western third of the continent, and (2) identify strategic locations to target for the development of new proxy records. We show that the two existing tree-ring records allow robust reconstructions of past hydroclimatic variability over spatially broad areas (i.e. > 3° × 3°) in inland north- and south-western Australia. Our results reveal synchronous periods of drought and wet conditions between the inland northern and southern regions of western Australia as well as a generally anti-phase relationship with hydroclimate in eastern Australia over the last two centuries. The inclusion of 174 tree-ring proxy records from Tasmania, New Zealand and Indonesia and a coral record from Queensland did not improve the reconstruction potential over western Australia. However, our findings suggest that the addition of relatively few new proxy records from key locations in western Australia that currently have low reconstruction skill will enable the development of a comprehensive drought atlas for the region, and provide a critical link to the drought atlases of monsoonal Asia and eastern Australia and New Zealand.

Non-uniform approximations for sums of discrete m-dependent random variables

OpenAIRE

Vellaisamy, P.; Cekanavicius, V.

2013-01-01

Non-uniform estimates are obtained for Poisson, compound Poisson, translated Poisson, negative binomial and binomial approximations to sums of of m-dependent integer-valued random variables. Estimates for Wasserstein metric also follow easily from our results. The results are then exemplified by the approximation of Poisson binomial distribution, 2-runs and $m$-dependent $(k_1,k_2)$-events.

An R package for spatial coverage sampling and random sampling from compact geographical strata by k-means

NARCIS (Netherlands)

Walvoort, D.J.J.; Brus, D.J.; Gruijter, de J.J.

2010-01-01

Both for mapping and for estimating spatial means of an environmental variable, the accuracy of the result will usually be increased by dispersing the sample locations so that they cover the study area as uniformly as possible. We developed a new R package for designing spatial coverage samples for

Spatial variability of isoproturon mineralizing activity within an agricultural field: Geostatistical analysis of simple physicochemical and microbiological soil parameters

Energy Technology Data Exchange (ETDEWEB)

El Sebai, T. [UMR Microbiologie et Geochimie des Sols, INRA/CMSE, 17 Rue Sully, BP 86510, 21065 Dijon Cedex (France); Lagacherie, B. [UMR Microbiologie et Geochimie des Sols, INRA/CMSE, 17 Rue Sully, BP 86510, 21065 Dijon Cedex (France); Soulas, G. [UMR Microbiologie et Geochimie des Sols, INRA/CMSE, 17 Rue Sully, BP 86510, 21065 Dijon Cedex (France); Martin-Laurent, F. [UMR Microbiologie et Geochimie des Sols, INRA/CMSE, 17 Rue Sully, BP 86510, 21065 Dijon Cedex (France)]. E-mail: fmartin@dijon.inra.fr

2007-02-15

We assessed the spatial variability of isoproturon mineralization in relation to that of physicochemical and biological parameters in fifty soil samples regularly collected along a sampling grid delimited across a 0.36 ha field plot (40 x 90 m). Only faint relationships were observed between isoproturon mineralization and the soil pH, microbial C biomass, and organic nitrogen. Considerable spatial variability was observed for six of the nine parameters tested (isoproturon mineralization rates, organic nitrogen, genetic structure of the microbial communities, soil pH, microbial biomass and equivalent humidity). The map of isoproturon mineralization rates distribution was similar to that of soil pH, microbial biomass, and organic nitrogen but different from those of structure of the microbial communities and equivalent humidity. Geostatistics revealed that the spatial heterogeneity in the rate of degradation of isoproturon corresponded to that of soil pH and microbial biomass. - In field spatial variation of isoproturon mineralization mainly results from the spatial heterogeneity of soil pH and microbial C biomass.

Spatial variability of isoproturon mineralizing activity within an agricultural field: Geostatistical analysis of simple physicochemical and microbiological soil parameters

International Nuclear Information System (INIS)

El Sebai, T.; Lagacherie, B.; Soulas, G.; Martin-Laurent, F.

2007-01-01

We assessed the spatial variability of isoproturon mineralization in relation to that of physicochemical and biological parameters in fifty soil samples regularly collected along a sampling grid delimited across a 0.36 ha field plot (40 x 90 m). Only faint relationships were observed between isoproturon mineralization and the soil pH, microbial C biomass, and organic nitrogen. Considerable spatial variability was observed for six of the nine parameters tested (isoproturon mineralization rates, organic nitrogen, genetic structure of the microbial communities, soil pH, microbial biomass and equivalent humidity). The map of isoproturon mineralization rates distribution was similar to that of soil pH, microbial biomass, and organic nitrogen but different from those of structure of the microbial communities and equivalent humidity. Geostatistics revealed that the spatial heterogeneity in the rate of degradation of isoproturon corresponded to that of soil pH and microbial biomass. - In field spatial variation of isoproturon mineralization mainly results from the spatial heterogeneity of soil pH and microbial C biomass

Spatial variability of chemical and physical attributes of dystrophic Red-Yellow Latosol in no tillage

Directory of Open Access Journals (Sweden)

João Vidal de Negreiros Neto

2014-02-01

Full Text Available Knowledge of spatial variability in chemical and physical properties of the soil is very important, especially for precision agriculture. Geostatistics is seeking to improve techniques that can enable the correct and responsible use of soil. So during the agricultural year 2011/2012 in an area of direct planting the corn crop in the municipality of Gurupi (TO, in the Brazilian Cerrado, aimed to analyze the spatial variability of chemical and physical properties in a Typic Dystrophic tillage. Was installed sampling grid for the collection of soil, with 100 sampling points in an area of 1755m2. The contents of available phosphorus, organic matter, pH (H2O, concentrations of K +, Ca2+, Mg2+, the sum of values and base saturation (BS, V at depths of 0-0.20 m, and resistance to penetration (RP at depths 0-0.05 m, 0.05-0.10 m, 0.10-0.20 m and 0.20-0.40 m and bulk density (Ds. We conducted a descriptive analysis classic, with the aid of statistical software ASSISTAT, and then were modeled semivariograms for all attributes, resulting in their cross-validation and kriging maps. The chemical and physical properties of soil, except the base saturation (V, spatial dependence. Probably the discontinuity of the spatial dependence of Vvalue, is due to fertility management over the years.

Spatial and temporal variability of Mediterranean drought events

Science.gov (United States)

Trigo, R.; Sousa, P.; Nieto, R.; Gimeno, L.

2009-04-01

The original Palmer Drought Severity Index (PDSI) and a recent adaptation to European soil characteristics, the Self Calibrated PDSI (or scPDSI) proposed by Schrier et al (2005) were used. We have computed monthly, seasonal and annual trends between 1901 and 2000 but also for the first and second halves of the 20th century. Results were represented only when achieving a minimum level of statistical significance (either 5% or 10% using a Mann-Kendall test) and confirm that the majority of the western and central Mediterranean is getting drier in the last decades of the 20th century while Turkey is generally getting wetter (Trigo et al., 2006). The spatio-temporal variability of these indices was evaluated with an EOF analysis, in order to reduce the large dimensionality of the fields under analysis. Spatial representation of the first EOF patterns shows that EOF 1 covers the entire Mediterranean basin (16.4% of EV), while EOF2 is dominated by a W-E dipole (10% EV). The following EOF patterns present smaller scale features, and explain smaller amounts of variance. The EOF patterns have also facilitated the definition of four sub-regions with large socio-economic relevance: 1) Iberia, 2) Italian Peninsula, 3) Balkans and 4) Turkey. The inter-annual variability of the regional spatial droughts indices for each region was analyzed separately. We have also performed an evaluation of their eventual links with large-scale atmospheric circulation indices that affect the Mediterranean basin, namely the NAO, EA, and SCAND. Finally we have evaluated the main sources of moisture affecting two drought prone areas in the western (Iberia) and eastern (Balkans) Mediterranean. This analysis was performed by means of backward tracking the air masses that ultimately reach these two regions using the Lagrangian particle dispersion model FLEXPART (Stohl et al., 1998) and meteorological analysis data from the ECMWF to track atmospheric moisture. This was done for a five-year period (2000

Determination of spatially dependent transfer function of zero power reactor by using pseudo-random incentive

International Nuclear Information System (INIS)

Kostic, Lj.

1973-01-01

Specially constructed fast reactivity oscillator was stimulating the zero power reactor by a stimulus which caused pseudo-random reactivity changes. Measuring system included stochastic oscillator BCR-1 supplied by pseudo-random pulses from noise generator GBS-16, instrumental tape-recorder, system for data acquisition and digital computer ZUSE-Z-23. For measuring the spatially dependent transfer function, reactor response was measured at a number of different positions of stochastic oscillator and ionization chamber. In order to keep the reactor system linear, experiment was limited to small reactivity fluctuations. Experimental results were compared to theoretical ones

Multilevel discretized random field models with 'spin' correlations for the simulation of environmental spatial data

International Nuclear Information System (INIS)

Žukovič, Milan; Hristopulos, Dionissios T

2009-01-01

A current problem of practical significance is how to analyze large, spatially distributed, environmental data sets. The problem is more challenging for variables that follow non-Gaussian distributions. We show by means of numerical simulations that the spatial correlations between variables can be captured by interactions between 'spins'. The spins represent multilevel discretizations of environmental variables with respect to a number of pre-defined thresholds. The spatial dependence between the 'spins' is imposed by means of short-range interactions. We present two approaches, inspired by the Ising and Potts models, that generate conditional simulations of spatially distributed variables from samples with missing data. Currently, the sampling and simulation points are assumed to be at the nodes of a regular grid. The conditional simulations of the 'spin system' are forced to respect locally the sample values and the system statistics globally. The second constraint is enforced by minimizing a cost function representing the deviation between normalized correlation energies of the simulated and the sample distributions. In the approach based on the N c -state Potts model, each point is assigned to one of N c classes. The interactions involve all the points simultaneously. In the Ising model approach, a sequential simulation scheme is used: the discretization at each simulation level is binomial (i.e., ± 1). Information propagates from lower to higher levels as the simulation proceeds. We compare the two approaches in terms of their ability to reproduce the target statistics (e.g., the histogram and the variogram of the sample distribution), to predict data at unsampled locations, as well as in terms of their computational complexity. The comparison is based on a non-Gaussian data set (derived from a digital elevation model of the Walker Lake area, Nevada, USA). We discuss the impact of relevant simulation parameters, such as the domain size, the number of

Impact of Urbanization on Spatial Variability of Rainfall-A case study of Mumbai city with WRF Model

Science.gov (United States)

Mathew, M.; Paul, S.; Devanand, A.; Ghosh, S.

2015-12-01

Urban precipitation enhancement has been identified over many cities in India by previous studies conducted. Anthropogenic effects such as change in land cover from hilly forest areas to flat topography with solid concrete infrastructures has certain effect on the local weather, the same way the greenhouse gas has on climate change. Urbanization could alter the large scale forcings to such an extent that it may bring about temporal and spatial changes in the urban weather. The present study investigate the physical processes involved in urban forcings, such as the effect of sudden increase in wind velocity travelling through the channel space in between the dense array of buildings, which give rise to turbulence and air mass instability in urban boundary layer and in return alters the rainfall distribution as well as rainfall initiation. A numerical model study is conducted over Mumbai metropolitan city which lies on the west coast of India, to assess the effect of urban morphology on the increase in number of extreme rainfall events in specific locations. An attempt has been made to simulate twenty extreme rainfall events that occurred over the summer monsoon period of the year 2014 using high resolution WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) model to assess the urban land cover mechanisms that influences precipitation variability over this spatially varying urbanized region. The result is tested against simulations with altered land use. The correlation of precipitation with spatial variability of land use is found using a detailed urban land use classification. The initial and boundary conditions for running the model were obtained from the global model ECMWF(European Centre for Medium Range Weather Forecast) reanalysis data having a horizontal resolution of 0.75 °x 0.75°. The high resolution simulations show significant spatial variability in the accumulated rainfall, within a few kilometers itself. Understanding the spatial

Land agroecological quality assessment in conditions of high spatial soil cover variability at the Pereslavskoye Opolye.

Science.gov (United States)

Morev, Dmitriy; Vasenev, Ivan

2015-04-01

The essential spatial variability is mutual feature for most natural and man-changed soils at the Central region of European territory of Russia. The original spatial heterogeneity of forest soils has been further complicated by a specific land-use history and human impacts. For demand-driven land-use planning and decision making the quantitative analysis and agroecological interpretation of representative soil cover spatial variability is an important and challenging task that receives increasing attention from private companies, governmental and environmental bodies. Pereslavskoye Opolye is traditionally actively used in agriculture due to dominated high-quality cultivated soddy-podzoluvisols which are relatively reached in organic matter (especially for conditions of the North part at the European territory of Russia). However, the soil cover patterns are often very complicated even within the field that significantly influences on crop yield variability and have to be considered in farming system development and land agroecological quality evaluation. The detailed investigations of soil regimes and mapping of the winter rye yield have been carried in conditions of two representative fields with slopes sharply contrasted both in aspects and degrees. Rye biological productivity and weed infestation have been measured in elementary plots of 0.25 m2 with the following analysis the quality of the yield. In the same plot soil temperature and moisture have been measured by portable devices. Soil sampling was provided from three upper layers by drilling. The results of ray yield detailed mapping shown high differences both in average values and within-field variability on different slopes. In case of low-gradient slope (field 1) there is variability of ray yield from 39.4 to 44.8 dt/ha. In case of expressed slope (field 2) the same species of winter rye grown with the same technology has essentially lower yield and within-field variability from 20 to 29.6 dt/ha. The

Soil physics and the water management of spatially variable soils

International Nuclear Information System (INIS)

Youngs, E.G.

1983-01-01

The physics of macroscopic soil-water behaviour in inert porous materials has been developed by considering water flow to take place in a continuum. This requires the flow region to consist of an assembly of representative elementary volumes, repeated throughout space and small compared with the scale of observations. Soil-water behaviour in swelling soils may also be considered as a continuum phenomenon so long as the soil is saturated and swells and shrinks in the normal range. Macroscale heterogeneity superimposed on the inherent microscale heterogeneity can take many forms and may pose difficulties in the definition and measurement of soil physical properties and also in the development and use of predictive theories of soil-water behaviour. Thus, measurement techniques appropriate for uniform soils are often inappropriate, and criteria for soil-water management, obtained from theoretical considerations of behaviour in equivalent uniform soils, are not applicable without modification when there is soil heterogeneity. The spatial variability of soil-water properties is shown in results from field experiments concerned with water flow measurements; these illustrate both stochastic and deterministic heterogeneity in soil-water properties. Problems of water management of spatially variable soils when there is stochastic heterogeneity appear to present an insuperable problem in the application of theory. However, for soils showing deterministic heterogeneity, soil-water theory has been used in the solution of soil-water management problems. Thus, scaling using similar media theory has been applied to the infiltration of water into soils that vary over a catchment area. Also, the drain spacing to control the water-table height in soils in which the hydraulic conductivity varies with depth has been calculated using groundwater seepage theory. (author)

Stable Graphical Model Estimation with Random Forests for Discrete, Continuous, and Mixed Variables

OpenAIRE

Fellinghauer, Bernd; Bühlmann, Peter; Ryffel, Martin; von Rhein, Michael; Reinhardt, Jan D.

2011-01-01

A conditional independence graph is a concise representation of pairwise conditional independence among many variables. Graphical Random Forests (GRaFo) are a novel method for estimating pairwise conditional independence relationships among mixed-type, i.e. continuous and discrete, variables. The number of edges is a tuning parameter in any graphical model estimator and there is no obvious number that constitutes a good choice. Stability Selection helps choosing this parameter with respect to...

Spatial Variability of Physical Soil Quality Index of an Agricultural Field

Directory of Open Access Journals (Sweden)

Sheikh M. Fazle Rabbi

2014-01-01

Full Text Available A field investigation was carried out to evaluate the spatial variability of physical indicators of soil quality of an agricultural field and to construct a physical soil quality index (SQIP map. Surface soil samples were collected using 10  m×10 m grid from an Inceptisol on Ganges Tidal Floodplain of Bangladesh. Five physical soil quality indicators, soil texture, bulk density, porosity, saturated hydraulic conductivity (KS, and aggregate stability (measured as mean weight diameter, MWD were determined. The spatial structures of sand, clay, and KS were moderate but the structure was strong for silt, bulk density, porosity, and MWD. Each of the physical soil quality indicators was transformed into 0 and 1 using threshold criteria which are required for crop production. The transformed indicators were the combined into SQIP. The kriged SQIP map showed that the agricultural field studied could be divided into two parts having “good physical quality” and “poor physical soil quality.”

Oracle Efficient Variable Selection in Random and Fixed Effects Panel Data Models

DEFF Research Database (Denmark)

Kock, Anders Bredahl

This paper generalizes the results for the Bridge estimator of Huang et al. (2008) to linear random and fixed effects panel data models which are allowed to grow in both dimensions. In particular we show that the Bridge estimator is oracle efficient. It can correctly distinguish between relevant...... and irrelevant variables and the asymptotic distribution of the estimators of the coefficients of the relevant variables is the same as if only these had been included in the model, i.e. as if an oracle had revealed the true model prior to estimation. In the case of more explanatory variables than observations......, we prove that the Marginal Bridge estimator can asymptotically correctly distinguish between relevant and irrelevant explanatory variables. We do this without restricting the dependence between covariates and without assuming sub Gaussianity of the error terms thereby generalizing the results...

A cellular automata model of traffic flow with variable probability of randomization

International Nuclear Information System (INIS)

Zheng Wei-Fan; Zhang Ji-Ye

2015-01-01

Research on the stochastic behavior of traffic flow is important to understand the intrinsic evolution rules of a traffic system. By introducing an interactional potential of vehicles into the randomization step, an improved cellular automata traffic flow model with variable probability of randomization is proposed in this paper. In the proposed model, the driver is affected by the interactional potential of vehicles before him, and his decision-making process is related to the interactional potential. Compared with the traditional cellular automata model, the modeling is more suitable for the driver’s random decision-making process based on the vehicle and traffic situations in front of him in actual traffic. From the improved model, the fundamental diagram (flow–density relationship) is obtained, and the detailed high-density traffic phenomenon is reproduced through numerical simulation. (paper)

On bounds in Poisson approximation for distributions of independent negative-binomial distributed random variables.

Science.gov (United States)

Hung, Tran Loc; Giang, Le Truong

2016-01-01

Using the Stein-Chen method some upper bounds in Poisson approximation for distributions of row-wise triangular arrays of independent negative-binomial distributed random variables are established in this note.

Multiobjective Two-Stage Stochastic Programming Problems with Interval Discrete Random Variables

Directory of Open Access Journals (Sweden)

S. K. Barik

2012-01-01

Full Text Available Most of the real-life decision-making problems have more than one conflicting and incommensurable objective functions. In this paper, we present a multiobjective two-stage stochastic linear programming problem considering some parameters of the linear constraints as interval type discrete random variables with known probability distribution. Randomness of the discrete intervals are considered for the model parameters. Further, the concepts of best optimum and worst optimum solution are analyzed in two-stage stochastic programming. To solve the stated problem, first we remove the randomness of the problem and formulate an equivalent deterministic linear programming model with multiobjective interval coefficients. Then the deterministic multiobjective model is solved using weighting method, where we apply the solution procedure of interval linear programming technique. We obtain the upper and lower bound of the objective function as the best and the worst value, respectively. It highlights the possible risk involved in the decision-making tool. A numerical example is presented to demonstrate the proposed solution procedure.

Monitoring Spatial Variability and Temporal Dynamics of Phragmites Using Unmanned Aerial Vehicles

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Viktor R. Tóth

2018-06-01

Full Text Available Littoral zones of freshwater lakes are exposed to environmental impacts from both terrestrial and aquatic sides, while substantial anthropogenic pressure also affects the high spatial, and temporal variability of the ecotone. In this study, the possibility of monitoring seasonal and spatial changes in reed (Phragmites australis stands using an unmanned aerial vehicle (UAV based remote sensing technique was examined. Stands in eutrophic and mesotrophic parts of Lake Balaton including not deteriorating (stable and deteriorating (die-back patches, were tracked throughout the growing season using a UAV equipped with a Normalized Difference Vegetation Index (NDVI camera. Photophysiological parameters of P. australis were also measured with amplitude modulated fluorescence. Parameters characterizing the dynamics of seasonal changes in NDVI data were used for phenological comparison of eutrophic and mesotrophic, stable and die-back, terrestrial and aquatic, mowed and not-mowed patches of reed. It was shown that stable Phragmites plants from the eutrophic part of the lake reached specific phenological stages up to 3.5 days earlier than plants from the mesotrophic part of the lake. The phenological changes correlated with trophic (total and nitrate-nitrite nitrogen and physical (organic C and clay content properties of the sediment, while only minor relationships with air and water temperature were found. Phenological differences between the stable and die-back stands were even more pronounced, with ~34% higher rates of NDVI increase in stable than die-back patches, while the period of NDVI increase was 16 days longer. Aquatic and terrestrial parts of reed stands showed no phenological differences, although intermediate areas (shallow water parts of stands were found to be less vigorous. Winter mowing of dried Phragmites sped up sprouting and growth of reed in the spring. This study showed that remote sensing-derived photophysiological and phenological

Spatial and temporal variability of thermohaline properties in the Bay of Koper (northern Adriatic Sea)

Science.gov (United States)

Soczka Mandac, Rok; Žagar, Dušan; Faganeli, Jadran

2013-04-01

In this study influence of fresh water discharge on the spatial and temporal variability of thermohaline (TH) conditions is explored for the Bay of Koper (Bay). The Bay is subject to different driving agents: wind stress (bora, sirocco), tidal and seiches effect, buoyancy fluxes, general circulation of the Adriatic Sea and discharge of the Rizana and Badaševica rivers. These rivers have torrential characteristics that are hard to forecast in relation to meteorological events (precipitation). Therefore, during episodic events the spatial and temporal variability of TH properties in the Bay is difficult to determine [1]. Measurements of temperature, salinity and turbidity were conducted monthly on 35 sampling points in the period: June 2011 - December 2012. The data were processed and spatial interpolated with an objective analysis method. Furthermore, empirical orthogonal function analysis (EOF) [2] was applied to investigate spatial and temporal TH variations. Strong horizontal and vertical stratification was observed in the beginning of June 2011 due to high fresh water discharge of the Rizana (31 m3/s) and Badaševica (2 m3/s) rivers. The horizontal gradient (ΔT = 6°C) was noticed near the mouth of the Rizana river. Similar pattern was identified for salinity field on the boundary of the front where the gradient was ΔS = 20 PSU. Vertical temperature gradient was ΔT = 4°C while salinity gradient was ΔS = 18 PSU in the subsurface layer at depth of 3 m. Spatial analysis of the first principal component (86% of the total variance) shows uniform temperature distribution in the surface layer (1m) during the studied period. Furthermore, temporal variability of temperature shows seasonal variation with a minimum in February and maximum in August. This confirms that episodic events have a negligible effect on spatial and temporal variation of temperature in the subsurface layer. Further analysis will include application of EOF on the salinity, density and total

Generation of correlated finite alphabet waveforms using gaussian random variables

KAUST Repository

Jardak, Seifallah

2014-09-01

Correlated waveforms have a number of applications in different fields, such as radar and communication. It is very easy to generate correlated waveforms using infinite alphabets, but for some of the applications, it is very challenging to use them in practice. Moreover, to generate infinite alphabet constant envelope correlated waveforms, the available research uses iterative algorithms, which are computationally very expensive. In this work, we propose simple novel methods to generate correlated waveforms using finite alphabet constant and non-constant-envelope symbols. To generate finite alphabet waveforms, the proposed method map the Gaussian random variables onto the phase-shift-keying, pulse-amplitude, and quadrature-amplitude modulation schemes. For such mapping, the probability-density-function of Gaussian random variables is divided into M regions, where M is the number of alphabets in the corresponding modulation scheme. By exploiting the mapping function, the relationship between the cross-correlation of Gaussian and finite alphabet symbols is derived. To generate equiprobable symbols, the area of each region is kept same. If the requirement is to have each symbol with its own unique probability, the proposed scheme allows us that as well. Although, the proposed scheme is general, the main focus of this paper is to generate finite alphabet waveforms for multiple-input multiple-output radar, where correlated waveforms are used to achieve desired beampatterns. © 2014 IEEE.

Anomalous transport in disordered fracture networks: Spatial Markov model for dispersion with variable injection modes

Science.gov (United States)

Kang, Peter K.; Dentz, Marco; Le Borgne, Tanguy; Lee, Seunghak; Juanes, Ruben

2017-08-01

We investigate tracer transport on random discrete fracture networks that are characterized by the statistics of the fracture geometry and hydraulic conductivity. While it is well known that tracer transport through fractured media can be anomalous and particle injection modes can have major impact on dispersion, the incorporation of injection modes into effective transport modeling has remained an open issue. The fundamental reason behind this challenge is that-even if the Eulerian fluid velocity is steady-the Lagrangian velocity distribution experienced by tracer particles evolves with time from its initial distribution, which is dictated by the injection mode, to a stationary velocity distribution. We quantify this evolution by a Markov model for particle velocities that are equidistantly sampled along trajectories. This stochastic approach allows for the systematic incorporation of the initial velocity distribution and quantifies the interplay between velocity distribution and spatial and temporal correlation. The proposed spatial Markov model is characterized by the initial velocity distribution, which is determined by the particle injection mode, the stationary Lagrangian velocity distribution, which is derived from the Eulerian velocity distribution, and the spatial velocity correlation length, which is related to the characteristic fracture length. This effective model leads to a time-domain random walk for the evolution of particle positions and velocities, whose joint distribution follows a Boltzmann equation. Finally, we demonstrate that the proposed model can successfully predict anomalous transport through discrete fracture networks with different levels of heterogeneity and arbitrary tracer injection modes.

Using randomized variable practice in the treatment of childhood apraxia of speech.

Science.gov (United States)

Skelton, Steven L; Hagopian, Aubrie Lynn

2014-11-01

The purpose of this study was to determine if randomized variable practice, a central component of concurrent treatment, would be effective and efficient in treating childhood apraxia of speech (CAS). Concurrent treatment is a treatment program that takes the speech task hierarchy and randomizes it so that all tasks are worked on in one session. Previous studies have shown the treatment program to be effective and efficient in treating phonological and articulation disorders. The program was adapted to be used with children with CAS. A research design of multiple baselines across participants was used. Probes of generalization to untaught words were administered every fifth session. Three children, ranging in age from 4 to 6 years old, were the participants. Data were collected as percent correct productions during baseline, treatment, and probes of generalization of target sounds to untaught words and three-word phrases. All participants showed an increase in correct productions during treatment and during probes. Effect sizes (standard mean difference) for treatment were 3.61-5.00, and for generalization probes, they were 3.15-8.51. The results obtained from this study suggest that randomized variable practice as used in concurrent treatment can be adapted for use in treating children with CAS. Replication of this study with other children presenting CAS will be needed to establish generality of the findings.

Problems of variance reduction in the simulation of random variables

International Nuclear Information System (INIS)

Lessi, O.

1987-01-01

The definition of the uniform linear generator is given and some of the mostly used tests to evaluate the uniformity and the independence of the obtained determinations are listed. The problem of calculating, through simulation, some moment W of a random variable function is taken into account. The Monte Carlo method enables the moment W to be estimated and the estimator variance to be obtained. Some techniques for the construction of other estimators of W with a reduced variance are introduced

Passive Sampling to Capture the Spatial Variability of Coarse Particles by Composition in Cleveland, OH

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Passive samplers deployed at 25 sites for three week-long intervals were used to characterize spatial variability in the mass and composition of coarse particulate matter (PM10-2.5) in Cleveland, OH in summer 2008. The size and composition of individual particles deter...

Spatial and temporal variability of biophysical variables in southwestern France from airborne L-band radiometry

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E. Zakharova

2012-06-01

ISBA-A-gs grid cells tended to increase the CAROLS SSM spatial variability, up to 0.10 m³ m⁻³. Also, the grid cells characterised by a high vegetation cover heterogeneity presented higher standard deviation values, for both SSM and VOD.

Spatial variability of oceanic phycoerythrin spectral types derived from airborne laser-induced fluorescence emissions

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Hoge, Frank E.; Wright, C. Wayne; Kana, Todd M.; Swift, Robert N.; Yungel, James K.

1998-07-01

We report spatial variability of oceanic phycoerythrin spectral types detected by means of a blue spectral shift in airborne laser-induced fluorescence emission. The blue shift of the phycoerythrobilin fluorescence is known from laboratory studies to be induced by phycourobilin chromophore substitution at phycoerythrobilin chromophore sites in some strains of phycoerythrin-containing marine cyanobacteria. The airborne 532-nm laser-induced phycoerythrin fluorescence of the upper oceanic volume showed distinct segregation of cyanobacterial chromophore types in a flight transect from coastal water to the Sargasso Sea in the western North Atlantic. High phycourobilin levels were restricted to the oceanic (oligotrophic) end of the flight transect, in agreement with historical ship findings. These remotely observed phycoerythrin spectral fluorescence shifts have the potential to permit rapid, wide-area studies of the spatial variability of spectrally distinct cyanobacteria, especially across interfacial regions of coastal and oceanic water masses. Airborne laser-induced phytoplankton spectral fluorescence observations also further the development of satellite algorithms for passive detection of phytoplankton pigments. Optical modifications to the NASA Airborne Oceanographic Lidar are briefly described that permitted observation of the fluorescence spectral shifts.

Spatial variability of N, P, and K in rice field in Sawah Sempadan, Malaysia

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Saeed Mohamed Eltaib

2002-04-01

Full Text Available The variability of soil chemical properties such as total N, available P, and exchangeable K were examined on a 1.2 ha rice (Oryza sativa field. The soil (n = 72 samples were systematically taken from individual fields in Sawah Sempadan in thirty-six locations at two depths (0-20 and 20-30 cm. The Differential Global Positioning System (DGPS was used for locating the sample position. Geostatistical techniques were used to analyze the soil chemical properties variability of the samples that assist in site-specific management of the field. Results showed that areas of similarity were much greater for the soil chemical properties measured at the depth of (0-20 cm than that of the second lower (20- 30 cm. The ranges of the semivariogram for total N, available P, and exchangeable K were 12, and 13 m (0-20 cm, 12 and 38 m (20-30 cm, respectively. Point kriging calculated from the semivariogram was employed for spatial distribution map. The results suggested that soil chemical properties measured may be spatially dependent even within the small.

Inter-annual and spatial variability in hillslope runoff and mercury flux during spring snowmelt.

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Haynes, Kristine M; Mitchell, Carl P J

2012-08-01

Spring snowmelt is an important period of mercury (Hg) export from watersheds. Limited research has investigated the potential effects of climate variability on hydrologic and Hg fluxes during spring snowmelt. The purpose of this research was to assess the potential impacts of inter-annual climate variability on Hg mobility in forested uplands, as well as spatial variability in hillslope hydrology and Hg fluxes. We compared hydrological flows, Hg and solute mobility from three adjacent hillslopes in the S7 watershed of the Marcell Experimental Forest, Minnesota during two very different spring snowmelt periods: one following a winter (2009-2010) with severely diminished snow accumulation (snow water equivalent (SWE) = 48 mm) with an early melt, and a second (2010-2011) with significantly greater winter snow accumulation (SWE = 98 mm) with average to late melt timing. Observed inter-annual differences in total Hg (THg) and dissolved organic carbon (DOC) yields were predominantly flow-driven, as the proportion by which solute yields increased was the same as the increase in runoff. Accounting for inter-annual differences in flow, there was no significant difference in THg and DOC export between the two snowmelt periods. The spring 2010 snowmelt highlighted the important contribution of melting soil frost in the timing of a considerable portion of THg exported from the hillslope, accounting for nearly 30% of the THg mobilized. Differences in slope morphology and soil depths to the confining till layer were important in controlling the large observed spatial variability in hydrological flowpaths, transmissivity feedback responses, and Hg flux trends across the adjacent hillslopes.

Characterization of meter-scale spatial variability of riverbed hydraulic conductivity in a lowland river (Aa River, Belgium)

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Ghysels, Gert; Benoit, Sien; Awol, Henock; Jensen, Evan Patrick; Debele Tolche, Abebe; Anibas, Christian; Huysmans, Marijke

2018-04-01

An improved general understanding of riverbed heterogeneity is of importance for all groundwater modeling studies that include river-aquifer interaction processes. Riverbed hydraulic conductivity (K) is one of the main factors controlling river-aquifer exchange fluxes. However, the meter-scale spatial variability of riverbed K has not been adequately mapped as of yet. This study aims to fill this void by combining an extensive field measurement campaign focusing on both horizontal and vertical riverbed K with a detailed geostatistical analysis of the meter-scale spatial variability of riverbed K . In total, 220 slug tests and 45 standpipe tests were performed at two test sites along the Belgian Aa River. Omnidirectional and directional variograms (along and across the river) were calculated. Both horizontal and vertical riverbed K vary over several orders of magnitude and show significant meter-scale spatial variation. Horizontal K shows a bimodal distribution. Elongated zones of high horizontal K along the river course are observed at both sections, indicating a link between riverbed structures, depositional environment and flow regime. Vertical K is lognormally distributed and its spatial variability is mainly governed by the presence and thickness of a low permeable organic layer at the top of the riverbed. The absence of this layer in the center of the river leads to high vertical K and is related to scouring of the riverbed by high discharge events. Variograms of both horizontal and vertical K show a clear directional anisotropy with ranges along the river being twice as large as those across the river.

A soil-landscape framework for understanding spatial and temporal variability in biogeochemical processes in catchments

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McGuire, K. J.; Bailey, S. W.; Ross, D. S.

2017-12-01

Heterogeneity in biophysical properties within catchments challenges how we quantify and characterize biogeochemical processes and interpret catchment outputs. Interactions between the spatiotemporal variability of hydrological states and fluxes and soil development can spatially structure catchments, leading to a framework for understanding patterns in biogeochemical processes. In an upland, glaciated landscape at the Hubbard Brook Experimental Forest (HBEF) in New Hampshire, USA, we are embracing the structure and organization of soils to understand the spatial relations between runoff production zones, distinct soil-biogeochemical environments, and solute retention and release. This presentation will use observations from the HBEF to demonstrate that a soil-landscape framework is essential in understanding the spatial and temporal variability of biogeochemical processes in this catchment. Specific examples will include how laterally developed soils reveal the location of active runoff production zones and lead to gradients in primary mineral dissolution and the distribution of weathering products along hillslopes. Soil development patterns also highlight potential carbon and nitrogen cycling hotspots, differentiate acidic conditions, and affect the regulation of surface water quality. Overall, this work demonstrates the importance of understanding the landscape-level structural organization of soils in characterizing the variation and extent of biogeochemical processes that occur in catchments.

Spatial variability of soil carbon, pH, available phosphorous and potassium in organic farm located in Mediterranean Croatia

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Bogunović, Igor; Pereira, Paulo; Šeput, Miranda

2016-04-01

Soil organic carbon (SOC), pH, available phosphorus (P), and potassium (K) are some of the most important factors to soil fertility. These soil parameters are highly variable in space and time, with implications to crop production. The aim of this work is study the spatial variability of SOC, pH, P and K in an organic farm located in river Rasa valley (Croatia). A regular grid (100 x 100 m) was designed and 182 samples were collected on Silty Clay Loam soil. P, K and SOC showed moderate heterogeneity with coefficient of variation (CV) of 21.6%, 32.8% and 51.9%, respectively. Soil pH record low spatial variability with CV of 1.5%. Soil pH, P and SOC did not follow normal distribution. Only after a Box-Cox transformation, data respected the normality requirements. Directional exponential models were the best fitted and used to describe spatial autocorrelation. Soil pH, P and SOC showed strong spatial dependence with nugget to sill ratio with 13.78%, 0.00% and 20.29%, respectively. Only K recorded moderate spatial dependence. Semivariogram ranges indicate that future sampling interval could be 150 - 200 m in order to reduce sampling costs. Fourteen different interpolation models for mapping soil properties were tested. The method with lowest Root Mean Square Error was the most appropriated to map the variable. The results showed that radial basis function models (Spline with Tension and Completely Regularized Spline) for P and K were the best predictors, while Thin Plate Spline and inverse distance weighting models were the least accurate. The best interpolator for pH and SOC was the local polynomial with the power of 1, while the least accurate were Thin Plate Spline. According to soil nutrient maps investigated area record very rich supply with K while P supply was insufficient on largest part of area. Soil pH maps showed mostly neutral reaction while individual parts of alkaline soil indicate the possibility of penetration of seawater and salt accumulation in the

Dissociable effects of practice variability on learning motor and timing skills.

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Caramiaux, Baptiste; Bevilacqua, Frédéric; Wanderley, Marcelo M; Palmer, Caroline

2018-01-01

Motor skill acquisition inherently depends on the way one practices the motor task. The amount of motor task variability during practice has been shown to foster transfer of the learned skill to other similar motor tasks. In addition, variability in a learning schedule, in which a task and its variations are interweaved during practice, has been shown to help the transfer of learning in motor skill acquisition. However, there is little evidence on how motor task variations and variability schedules during practice act on the acquisition of complex motor skills such as music performance, in which a performer learns both the right movements (motor skill) and the right time to perform them (timing skill). This study investigated the impact of rate (tempo) variability and the schedule of tempo change during practice on timing and motor skill acquisition. Complete novices, with no musical training, practiced a simple musical sequence on a piano keyboard at different rates. Each novice was assigned to one of four learning conditions designed to manipulate the amount of tempo variability across trials (large or small tempo set) and the schedule of tempo change (randomized or non-randomized order) during practice. At test, the novices performed the same musical sequence at a familiar tempo and at novel tempi (testing tempo transfer), as well as two novel (but related) sequences at a familiar tempo (testing spatial transfer). We found that practice conditions had little effect on learning and transfer performance of timing skill. Interestingly, practice conditions influenced motor skill learning (reduction of movement variability): lower temporal variability during practice facilitated transfer to new tempi and new sequences; non-randomized learning schedule improved transfer to new tempi and new sequences. Tempo (rate) and the sequence difficulty (spatial manipulation) affected performance variability in both timing and movement. These findings suggest that there is a

Electrically tunable spatially variable switching in ferroelectric liquid crystal/water system

Science.gov (United States)

Choudhary, A.; Coondoo, I.; Prakash, J.; Sreenivas, K.; Biradar, A. M.

2009-04-01

An unusual switching phenomenon in the region outside conducting patterned area in ferroelectric liquid crystal (FLC) containing about 1-2 wt % of water has been observed. The presence of water in the studied heterogeneous system was confirmed by Fourier transform infrared spectroscopy. The observed optical studies have been emphasized on the "spatially variable switching" phenomenon of the molecules in the nonconducting region of the cell. The observed phenomenon is due to diffusion of water between the smectic layers of the FLC and the interaction of the curved electric field lines with the FLC molecules in the nonconducting region.

Spatial analysis of factors influencing long-term stress in the grizzly bear (Ursus arctos) population of Alberta, Canada.

Science.gov (United States)

Bourbonnais, Mathieu L; Nelson, Trisalyn A; Cattet, Marc R L; Darimont, Chris T; Stenhouse, Gordon B

2013-01-01

Non-invasive measures for assessing long-term stress in free ranging mammals are an increasingly important approach for understanding physiological responses to landscape conditions. Using a spatially and temporally expansive dataset of hair cortisol concentrations (HCC) generated from a threatened grizzly bear (Ursus arctos) population in Alberta, Canada, we quantified how variables representing habitat conditions and anthropogenic disturbance impact long-term stress in grizzly bears. We characterized spatial variability in male and female HCC point data using kernel density estimation and quantified variable influence on spatial patterns of male and female HCC stress surfaces using random forests. Separate models were developed for regions inside and outside of parks and protected areas to account for substantial differences in anthropogenic activity and disturbance within the study area. Variance explained in the random forest models ranged from 55.34% to 74.96% for males and 58.15% to 68.46% for females. Predicted HCC levels were higher for females compared to males. Generally, high spatially continuous female HCC levels were associated with parks and protected areas while low-to-moderate levels were associated with increased anthropogenic disturbance. In contrast, male HCC levels were low in parks and protected areas and low-to-moderate in areas with increased anthropogenic disturbance. Spatial variability in gender-specific HCC levels reveal that the type and intensity of external stressors are not uniform across the landscape and that male and female grizzly bears may be exposed to, or perceive, potential stressors differently. We suggest observed spatial patterns of long-term stress may be the result of the availability and distribution of foods related to disturbance features, potential sexual segregation in available habitat selection, and may not be influenced by sources of mortality which represent acute traumas. In this wildlife system and others

Spatial variability of sorghum forage yield and physical attributes of an Planosol = Variabilidade espacial da produtividade de sorgo e atributos físicos em um Planossolo.

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Rafael Montanari

2013-12-01

Full Text Available The cultivation of sorghum (Sorghum bicolor L. Moench is increasing in the Midwest region of Brazil with the aim of expanding the production of silage to be used in animal feed, with good adaptability to climatic conditions of the arid and semi-arid brazilian. The productive capacity of sorghum is influenced by soil physical properties (RP, UG, UV e DS, with these values appropriate to the development of the root system positively affect the productivity. In order to study the spatial and linear correlations between the yield of sorghum for forage and soil physical properties, an experiment was conducted in the Miranda city, MS, in an Planosol. The data were obtained by analysis of samples of plant (MVF and soil (RP, UG, UV e DS collected at random, having been demarcated using a GPS receiver 51 points in the cultivation area with irregular spacing. The attributes studied (plant and soil, and have spatial correlation, the variability between medium and high and well-defined spatial patterns, with a range between 130.0 and 352.0 m. The RP and UG were good indicators of soil physical quality, as for the productivity of green biomass forage sorghum. =

Spatial variability of nitrogen-15 and its relation to the variability of other soil properties

International Nuclear Information System (INIS)

Selles, F.; Karamanos, R.E.; Kachanoski, R.G.

1986-01-01

The spatial variability of natural 15 N abundance of a cultivated Chernozemic soil and its native prairie counterpart were smaller than that of total N, organic C, and the C/N ratio. Further, the number of samples required to estimate the true mean of total N with a given precision at various probability levels were twofold those required to estimate the true mean of total N with a given precision at various probability levels were twofold those required to determine the mean 15 N abundance of total soil N in the surface horizons may reflect the isotopic composition of the nitrogenous substances entering the soil system or changes in the isotopic composition of soil N due to humification processes, probably induced by variations in topographic and microrelief features of the soil

On the Distribution of Indefinite Quadratic Forms in Gaussian Random Variables

KAUST Repository

Al-Naffouri, Tareq Y.

2015-10-30

© 2015 IEEE. In this work, we propose a unified approach to evaluating the CDF and PDF of indefinite quadratic forms in Gaussian random variables. Such a quantity appears in many applications in communications, signal processing, information theory, and adaptive filtering. For example, this quantity appears in the mean-square-error (MSE) analysis of the normalized least-meansquare (NLMS) adaptive algorithm, and SINR associated with each beam in beam forming applications. The trick of the proposed approach is to replace inequalities that appear in the CDF calculation with unit step functions and to use complex integral representation of the the unit step function. Complex integration allows us then to evaluate the CDF in closed form for the zero mean case and as a single dimensional integral for the non-zero mean case. Utilizing the saddle point technique allows us to closely approximate such integrals in non zero mean case. We demonstrate how our approach can be extended to other scenarios such as the joint distribution of quadratic forms and ratios of such forms, and to characterize quadratic forms in isotropic distributed random variables.We also evaluate the outage probability in multiuser beamforming using our approach to provide an application of indefinite forms in communications.

Spatial variability of maximum annual daily rain under different return periods at the Rio de Janeiro state, Brazil

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Roriz Luciano Machado

2010-01-01

Full Text Available Knowledge of maximum daily rain and its return period in a region is an important tool to soil conservation, hydraulic engineering and preservation of road projects. The objective of this work was to evaluate the spatial variability of maximum annual daily rain considering different return periods, at the Rio de Janeiro State. The data set was composed by historical series of 119 rain gauges, for 36 years of observation. The return periods, estimated by Gumbel distribution, were 2, 5, 10, 25, 50 and 100 years. The spatial variability of the return periods was evaluated by semivariograms. All the return periods presented spatial dependence, with exponential and spherical model fitted to the experimental semivariograms. The parameters of the fitted semivariogram model were very similar; however, it was observed the presence of higher nugget effects for semivariograms of longer return periods. The values of maximum annual daily average rain in all the return periods increased from north to south and from countryside to the coast. In the region between the Serra do Mar range and the coast, besides increasing in magnitude, an increase in the spatial variability of the studied values with increasing return periods was also noticed. This behavior is probably caused by the orographic effect. The interpolated maps were more erratic for higher return periods and at the North, Northeast and Coastal Plain regions, in which the installation of new pluviometric stations are recommended.

The effects of spatial variability of the aggressiveness of soil on system reliability of corroding underground pipelines

International Nuclear Information System (INIS)

Sahraoui, Yacine; Chateauneuf, Alaa

2016-01-01

In this paper, a probabilistic methodology is presented for assessing the time-variant reliability of corroded underground pipelines subjected to space-variant soil aggressiveness. The Karhunen-Loève expansion is used to model the spatial variability of soil as a correlated stochastic field. The pipeline is considered as a series system for which the component and system failure probabilities are computed by Monte Carlo simulations. The probabilistic model provides a realistic time and space modelling of stochastic variations, leading to appropriate estimation of the lifetime distribution. The numerical analyses allow us to investigate the impact of various parameters on the reliability of underground pipelines, such as the soil aggressiveness, the pipe design variables, the soil correlation length and the pipeline length. The results show that neglecting the effect of spatial variability leads to pessimistic estimation of the residual lifetime and can lead to condemn prematurely the structure. - Highlights: • The role of soil heterogeneity in pipeline reliability assessment has been shown. • The impact of pipe length and soil correlation length has been examined. • The effect of the uncertainties related to design variables has been observed. • Pipe thickness design for homogeneous reliability has been proposed.

Optimal estimation of spatially variable recharge and transmissivity fields under steady-state groundwater flow. Part 2. Case study

Science.gov (United States)

Graham, Wendy D.; Neff, Christina R.

1994-05-01

The first-order analytical solution of the inverse problem for estimating spatially variable recharge and transmissivity under steady-state groundwater flow, developed in Part 1 is applied to the Upper Floridan Aquifer in NE Florida. Parameters characterizing the statistical structure of the log-transmissivity and head fields are estimated from 152 measurements of transmissivity and 146 measurements of hydraulic head available in the study region. Optimal estimates of the recharge, transmissivity and head fields are produced throughout the study region by conditioning on the nearest 10 available transmissivity measurements and the nearest 10 available head measurements. Head observations are shown to provide valuable information for estimating both the transmissivity and the recharge fields. Accurate numerical groundwater model predictions of the aquifer flow system are obtained using the optimal transmissivity and recharge fields as input parameters, and the optimal head field to define boundary conditions. For this case study, both the transmissivity field and the uncertainty of the transmissivity field prediction are poorly estimated, when the effects of random recharge are neglected.

Variability in population abundance is associated with thresholds between scaling regimes

Science.gov (United States)

Wardwell, D.; Allen, Craig R.

2009-01-01

Discontinuous structure in landscapes may result in discontinuous, aggregated species body-mass patterns, reflecting the scales of structure available to animal communities within a landscape. The edges of these body-mass aggregations reflect transitions between available scales of landscape structure. Such transitions, or scale breaks, are theoretically associated with increased biological variability. We hypothesized that variability in population abundance is greater in animal species near the edge of body-mass aggregations than it is in species that are situated in the interior of body-mass aggregations. We tested this hypothesis by examining both temporal and spatial variability in the abundance of species in the bird community of the Florida Everglades sub-ecoregion, USA. Analyses of both temporal and spatial variability in population abundance supported our hypothesis. Our results indicate that variability within complex systems may be non-random, and is heightened where transitions in scales of process and structure occur. This is the first explicit test of the hypothetical relationship between increased population variability and scale breaks. ?? 2009 by the author(s).

Effects of Uncertainty and Spatial Variability on Seepage into Drifts in the Yucca Mountain Total system Performance Assessment Model

International Nuclear Information System (INIS)

Kalinich, D. A.; Wilson, M. L.

2001-01-01

Seepage into the repository drifts is an important factor in total-system performance. Uncertainty and spatial variability are considered in the seepage calculations. The base-case results show 13.6% of the waste packages (WPs) have seepage. For 5th percentile uncertainty, 4.5% of the WPs have seepage and the seepage flow decreased by a factor of 2. For 95th percentile uncertainty, 21.5% of the WPs have seepage and the seepage flow increased by a factor of 2. Ignoring spatial variability resulted in seepage on 100% of the WPs, with a factor of 3 increase in the seepage flow

An edgeworth expansion for a sum of M-Dependent random variables

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Wan Soo Rhee

1985-01-01

Full Text Available Given a sequence X1,X2,…,Xn of m-dependent random variables with moments of order 3+α (0<α≦1, we give an Edgeworth expansion of the distribution of Sσ−1(S=X1+X2+…+Xn, σ2=ES2 under the assumption that E[exp(it Sσ1] is small away from the origin. The result is of the best possible order.

Assessing the Impact of Socioeconomic Variables on Small Area Variations in Suicide Outcomes in England

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Peter Congdon

2012-12-01

Full Text Available Ecological studies of suicide and self-harm have established the importance of area variables (e.g., deprivation, social fragmentation in explaining variations in suicide risk. However, there are likely to be unobserved influences on risk, typically spatially clustered, which can be modeled as random effects. Regression impacts may be biased if no account is taken of spatially structured influences on risk. Furthermore a default assumption of linear effects of area variables may also misstate or understate their impact. This paper considers variations in suicide outcomes for small areas across England, and investigates the impact on them of area socio-economic variables, while also investigating potential nonlinearity in their impact and allowing for spatially clustered unobserved factors. The outcomes are self-harm hospitalisations and suicide mortality over 6,781 Middle Level Super Output Areas.

The variability of the primeval forest's spatial pattern in the Babia Gora National Park

International Nuclear Information System (INIS)

Chrobaczek, U.; Jastrzebski, R.; Ziemniewicz, M.; Kaczor, D.; Widlak, M.; Lesiak, M.

2011-01-01

This paper analyzes the spatial variability of stand volume, species composition and regeneration in a primeval stand located in the lower maintain belt in the Babia Gora massif. These characteristics were surveyed on 259 circular plots (of a 7.0 m radius) located in a square grid 20 m · 20 m on the total area 10.36 ha. (authors)

Not just another variable: untangling the spatialities of power in social-ecological systems

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Micah L. Ingalls

2017-09-01

Full Text Available Increased attention has been paid to how the spatial dimensions of social-ecological systems are formative in shaping their ability to negotiate change and remain resilient. This paper moves this research further by exploring how diverse forms of power play a crucial role in shaping these spatial dimensions and the production of social-ecological outcomes. Grounding these explorations in a National Protected Area in Lao PDR, this paper explores how power relationships operate through the spatial and temporal domains of complex systems. Findings suggest (at least four important insights: (1 the exercise of power materializes in policies and programs and becomes written onto the spaces of social-ecological systems through boundary creation, zonation, and other social processes that (redefine spatial meanings; these meanings vary by social actor; (2 policies and programs map out unevenly across space and time as they interact with antecedent social-ecological conditions in ways that preclude linear causal relationships between policy and outcomes; (3 although local in their expression, spatialized disputes in social-ecological systems draw on cross-scalar discourses and networks of power to bolster, undermine, and (delegitimize competing environmental and social narratives; and (4 however powerful institutions and actor-networks may be, they are never fully hegemonic as they are attenuated by other discourses and operations of power, although these all play out across a highly uneven sociopolitical terrain. Paying greater attention to the spatial and temporal dynamics of power may be much more than a project of introducing yet another variable into the already complex admixture of analytic elements. Rather, by rendering these explicit as objects of analysis, common insights may change entirely or even be overturned.

Analysis of Secret Key Randomness Exploiting the Radio Channel Variability

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Taghrid Mazloum

2015-01-01

Full Text Available A few years ago, physical layer based techniques have started to be considered as a way to improve security in wireless communications. A well known problem is the management of ciphering keys, both regarding the generation and distribution of these keys. A way to alleviate such difficulties is to use a common source of randomness for the legitimate terminals, not accessible to an eavesdropper. This is the case of the fading propagation channel, when exact or approximate reciprocity applies. Although this principle has been known for long, not so many works have evaluated the effect of radio channel properties in practical environments on the degree of randomness of the generated keys. To this end, we here investigate indoor radio channel measurements in different environments and settings at either 2.4625 GHz or 5.4 GHz band, of particular interest for WIFI related standards. Key bits are extracted by quantizing the complex channel coefficients and their randomness is evaluated using the NIST test suite. We then look at the impact of the carrier frequency, the channel variability in the space, time, and frequency degrees of freedom used to construct a long secret key, in relation to the nature of the radio environment such as the LOS/NLOS character.

Scale-dependent spatial variability in peatland lead pollution in the southern Pennines, UK.

Science.gov (United States)

Rothwell, James J; Evans, Martin G; Lindsay, John B; Allott, Timothy E H

2007-01-01

Increasingly, within-site and regional comparisons of peatland lead pollution have been undertaken using the inventory approach. The peatlands of the Peak District, southern Pennines, UK, have received significant atmospheric inputs of lead over the last few hundred years. A multi-core study at three peatland sites in the Peak District demonstrates significant within-site spatial variability in industrial lead pollution. Stochastic simulations reveal that 15 peat cores are required to calculate reliable lead inventories at the within-site and within-region scale for this highly polluted area of the southern Pennines. Within-site variability in lead pollution is dominant at the within-region scale. The study demonstrates that significant errors may be associated with peatland lead inventories at sites where only a single peat core has been used to calculate an inventory. Meaningful comparisons of lead inventories at the regional or global scale can only be made if the within-site variability of lead pollution has been quantified reliably.

Fourier transform infrared spectroscopy microscopic imaging classification based on spatial-spectral features

Science.gov (United States)

Liu, Lian; Yang, Xiukun; Zhong, Mingliang; Liu, Yao; Jing, Xiaojun; Yang, Qin

2018-04-01

The discrete fractional Brownian incremental random (DFBIR) field is used to describe the irregular, random, and highly complex shapes of natural objects such as coastlines and biological tissues, for which traditional Euclidean geometry cannot be used. In this paper, an anisotropic variable window (AVW) directional operator based on the DFBIR field model is proposed for extracting spatial characteristics of Fourier transform infrared spectroscopy (FTIR) microscopic imaging. Probabilistic principal component analysis first extracts spectral features, and then the spatial features of the proposed AVW directional operator are combined with the former to construct a spatial-spectral structure, which increases feature-related information and helps a support vector machine classifier to obtain more efficient distribution-related information. Compared to Haralick’s grey-level co-occurrence matrix, Gabor filters, and local binary patterns (e.g. uniform LBPs, rotation-invariant LBPs, uniform rotation-invariant LBPs), experiments on three FTIR spectroscopy microscopic imaging datasets show that the proposed AVW directional operator is more advantageous in terms of classification accuracy, particularly for low-dimensional spaces of spatial characteristics.

The Economics of Storage, Transmission and Drought: Integrating Variable Wind Power into Spatially Separated Electricity Grids

NARCIS (Netherlands)

Scora, H.; Sopinka, A.; Kooten, van G.C.

2012-01-01

To mitigate the high variability of wind and make it a more viable renewable energy source, observers recommend greater integration of spatially-separated electrical grids, with high transmission lines linking load centers, scattered wind farms and hydro storage sites. In this study, we examine the

Spatial variability in floodplain sedimentation: the use of generalized linear mixed-effects models

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A. Cabezas

2010-08-01

Full Text Available Sediment, Total Organic Carbon (TOC and total nitrogen (TN accumulation during one overbank flood (1.15 y return interval were examined at one reach of the Middle Ebro River (NE Spain for elucidating spatial patterns. To achieve this goal, four areas with different geomorphological features and located within the study reach were examined by using artificial grass mats. Within each area, 1 m² study plots consisting of three pseudo-replicates were placed in a semi-regular grid oriented perpendicular to the main channel. TOC, TN and Particle-Size composition of deposited sediments were examined and accumulation rates estimated. Generalized linear mixed-effects models were used to analyze sedimentation patterns in order to handle clustered sampling units, specific-site effects and spatial self-correlation between observations. Our results confirm the importance of channel-floodplain morphology and site micro-topography in explaining sediment, TOC and TN deposition patterns, although the importance of other factors as vegetation pattern should be included in further studies to explain small-scale variability. Generalized linear mixed-effect models provide a good framework to deal with the high spatial heterogeneity of this phenomenon at different spatial scales, and should be further investigated in order to explore its validity when examining the importance of factors such as flood magnitude or suspended sediment concentration.

A simulation study on estimating biomarker-treatment interaction effects in randomized trials with prognostic variables.

Science.gov (United States)

Haller, Bernhard; Ulm, Kurt

2018-02-20

To individualize treatment decisions based on patient characteristics, identification of an interaction between a biomarker and treatment is necessary. Often such potential interactions are analysed using data from randomized clinical trials intended for comparison of two treatments. Tests of interactions are often lacking statistical power and we investigated if and how a consideration of further prognostic variables can improve power and decrease the bias of estimated biomarker-treatment interactions in randomized clinical trials with time-to-event outcomes. A simulation study was performed to assess how prognostic factors affect the estimate of the biomarker-treatment interaction for a time-to-event outcome, when different approaches, like ignoring other prognostic factors, including all available covariates or using variable selection strategies, are applied. Different scenarios regarding the proportion of censored observations, the correlation structure between the covariate of interest and further potential prognostic variables, and the strength of the interaction were considered. The simulation study revealed that in a regression model for estimating a biomarker-treatment interaction, the probability of detecting a biomarker-treatment interaction can be increased by including prognostic variables that are associated with the outcome, and that the interaction estimate is biased when relevant prognostic variables are not considered. However, the probability of a false-positive finding increases if too many potential predictors are included or if variable selection is performed inadequately. We recommend undertaking an adequate literature search before data analysis to derive information about potential prognostic variables and to gain power for detecting true interaction effects and pre-specifying analyses to avoid selective reporting and increased false-positive rates.

Spatial variability of isoproturon mineralizing activity within an agricultural field: geostatistical analysis of simple physicochemical and microbiological soil parameters.

Science.gov (United States)

El Sebai, T; Lagacherie, B; Soulas, G; Martin-Laurent, F

2007-02-01

We assessed the spatial variability of isoproturon mineralization in relation to that of physicochemical and biological parameters in fifty soil samples regularly collected along a sampling grid delimited across a 0.36 ha field plot (40 x 90 m). Only faint relationships were observed between isoproturon mineralization and the soil pH, microbial C biomass, and organic nitrogen. Considerable spatial variability was observed for six of the nine parameters tested (isoproturon mineralization rates, organic nitrogen, genetic structure of the microbial communities, soil pH, microbial biomass and equivalent humidity). The map of isoproturon mineralization rates distribution was similar to that of soil pH, microbial biomass, and organic nitrogen but different from those of structure of the microbial communities and equivalent humidity. Geostatistics revealed that the spatial heterogeneity in the rate of degradation of isoproturon corresponded to that of soil pH and microbial biomass.

Comparing daily temperature averaging methods: the role of surface and atmosphere variables in determining spatial and seasonal variability

Science.gov (United States)

Bernhardt, Jase; Carleton, Andrew M.

2018-05-01

The two main methods for determining the average daily near-surface air temperature, twice-daily averaging (i.e., [Tmax+Tmin]/2) and hourly averaging (i.e., the average of 24 hourly temperature measurements), typically show differences associated with the asymmetry of the daily temperature curve. To quantify the relative influence of several land surface and atmosphere variables on the two temperature averaging methods, we correlate data for 215 weather stations across the Contiguous United States (CONUS) for the period 1981-2010 with the differences between the two temperature-averaging methods. The variables are land use-land cover (LULC) type, soil moisture, snow cover, cloud cover, atmospheric moisture (i.e., specific humidity, dew point temperature), and precipitation. Multiple linear regression models explain the spatial and monthly variations in the difference between the two temperature-averaging methods. We find statistically significant correlations between both the land surface and atmosphere variables studied with the difference between temperature-averaging methods, especially for the extreme (i.e., summer, winter) seasons (adjusted R2 > 0.50). Models considering stations with certain LULC types, particularly forest and developed land, have adjusted R2 values > 0.70, indicating that both surface and atmosphere variables control the daily temperature curve and its asymmetry. This study improves our understanding of the role of surface and near-surface conditions in modifying thermal climates of the CONUS for a wide range of environments, and their likely importance as anthropogenic forcings—notably LULC changes and greenhouse gas emissions—continues.

Exploring prediction uncertainty of spatial data in geostatistical and machine learning Approaches

Science.gov (United States)

Klump, J. F.; Fouedjio, F.

2017-12-01

Geostatistical methods such as kriging with external drift as well as machine learning techniques such as quantile regression forest have been intensively used for modelling spatial data. In addition to providing predictions for target variables, both approaches are able to deliver a quantification of the uncertainty associated with the prediction at a target location. Geostatistical approaches are, by essence, adequate for providing such prediction uncertainties and their behaviour is well understood. However, they often require significant data pre-processing and rely on assumptions that are rarely met in practice. Machine learning algorithms such as random forest regression, on the other hand, require less data pre-processing and are non-parametric. This makes the application of machine learning algorithms to geostatistical problems an attractive proposition. The objective of this study is to compare kriging with external drift and quantile regression forest with respect to their ability to deliver reliable prediction uncertainties of spatial data. In our comparison we use both simulated and real world datasets. Apart from classical performance indicators, comparisons make use of accuracy plots, probability interval width plots, and the visual examinations of the uncertainty maps provided by the two approaches. By comparing random forest regression to kriging we found that both methods produced comparable maps of estimated values for our variables of interest. However, the measure of uncertainty provided by random forest seems to be quite different to the measure of uncertainty provided by kriging. In particular, the lack of spatial context can give misleading results in areas without ground truth data. These preliminary results raise questions about assessing the risks associated with decisions based on the predictions from geostatistical and machine learning algorithms in a spatial context, e.g. mineral exploration.

Influence of Surface Roughness Spatial Variability and Temporal Dynamics on the Retrieval of Soil Moisture from SAR Observations

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Jesús Álvarez-Mozos

2009-01-01

Full Text Available Radar-based surface soil moisture retrieval has been subject of intense research during the last decades. However, several difficulties hamper the operational estimation of soil moisture based on currently available spaceborne sensors. The main difficulty experienced so far results from the strong influence of other surface characteristics, mainly roughness, on the backscattering coefficient, which hinders the soil moisture inversion. This is especially true for single configuration observations where the solution to the surface backscattering problem is ill-posed. Over agricultural areas cultivated with winter cereal crops, roughness can be assumed to remain constant along the growing cycle allowing the use of simplified approaches that facilitate the estimation of the moisture content of soils. However, the field scale spatial variability and temporal variations of roughness can introduce errors in the estimation of soil moisture that are difficult to evaluate. The objective of this study is to assess the impact of roughness spatial variability and roughness temporal variations on the retrieval of soil moisture from radar observations. A series of laser profilometer measurements were performed over several fields in an experimental watershed from September 2004 to March 2005. The influence of the observed roughness variability and its temporal variations on the retrieval of soil moisture is studied using simulations performed with the Integral Equation Model, considering different sensor configurations. Results show that both field scale roughness spatial variability and its temporal variations are aspects that need to be taken into account, since they can introduce large errors on the retrieved soil moisture values.

Modeling the Impacts of Spatial Heterogeneity in the Castor Watershed on Runoff, Sediment, and Phosphorus Loss Using SWAT: I. Impacts of Spatial Variability of Soil Properties.

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Boluwade, Alaba; Madramootoo, Chandra

2013-01-01

Spatial accuracy of hydrologic modeling inputs influences the output from hydrologic models. A pertinent question is to know the optimal level of soil sampling or how many soil samples are needed for model input, in order to improve model predictions. In this study, measured soil properties were clustered into five different configurations as inputs to the Soil and Water Assessment Tool (SWAT) simulation of the Castor River watershed (11-km 2 area) in southern Quebec, Canada. SWAT is a process-based model that predicts the impacts of climate and land use management on water yield, sediment, and nutrient fluxes. SWAT requires geographical information system inputs such as the digital elevation model as well as soil and land use maps. Mean values of soil properties are used in soil polygons (soil series); thus, the spatial variability of these properties is neglected. The primary objective of this study was to quantify the impacts of spatial variability of soil properties on the prediction of runoff, sediment, and total phosphorus using SWAT. The spatial clustering of the measured soil properties was undertaken using the regionalized with dynamically constrained agglomerative clustering and partitioning method. Measured soil data were clustered into 5, 10, 15, 20, and 24 heterogeneous regions. Soil data from the Castor watershed which have been used in previous studies was also set up and termed "Reference". Overall, there was no significant difference in runoff simulation across the five configurations including the reference. This may be attributable to SWAT's use of the soil conservation service curve number method in flow simulation. Therefore having high spatial resolution inputs for soil data may not necessarily improve predictions when they are used in hydrologic modeling.

Simulating maize yield and bomass with spatial variability of soil field capacity

Science.gov (United States)

Ma, Liwang; Ahuja, Lajpat; Trout, Thomas; Nolan, Bernard T.; Malone, Robert W.

2015-01-01

Spatial variability in field soil properties is a challenge for system modelers who use single representative values, such as means, for model inputs, rather than their distributions. In this study, the root zone water quality model (RZWQM2) was first calibrated for 4 yr of maize (Zea mays L.) data at six irrigation levels in northern Colorado and then used to study spatial variability of soil field capacity (FC) estimated in 96 plots on maize yield and biomass. The best results were obtained when the crop parameters were fitted along with FCs, with a root mean squared error (RMSE) of 354 kg ha–1 for yield and 1202 kg ha–1 for biomass. When running the model using each of the 96 sets of field-estimated FC values, instead of calibrating FCs, the average simulated yield and biomass from the 96 runs were close to measured values with a RMSE of 376 kg ha–1 for yield and 1504 kg ha–1 for biomass. When an average of the 96 FC values for each soil layer was used, simulated yield and biomass were also acceptable with a RMSE of 438 kg ha–1 for yield and 1627 kg ha–1 for biomass. Therefore, when there are large numbers of FC measurements, an average value might be sufficient for model inputs. However, when the ranges of FC measurements were known for each soil layer, a sampled distribution of FCs using the Latin hypercube sampling (LHS) might be used for model inputs.

Coastal upwelling south of Madagascar: Temporal and spatial variability

Science.gov (United States)

Ramanantsoa, Juliano D.; Krug, M.; Penven, P.; Rouault, M.; Gula, J.

2018-02-01

Madagascar's southern coastal marine zone is a region of high biological productivity which supports a wide range of marine ecosystems, including fisheries. This high biological productivity is attributed to coastal upwelling. This paper provides new insights on the structure, variability and drivers of the coastal upwelling south of Madagascar. Satellite remote sensing is used to characterize the spatial extent and strength of the coastal upwelling. A front detection algorithm is applied to thirteen years of Multi-scale Ultra-high Resolution (MUR) Sea Surface Temperatures (SST) and an upwelling index is calculated. The influence of winds and ocean currents as drivers of the upwelling is investigated using satellite, in-situ observations, and a numerical model. Results reveal the presence of two well-defined upwelling cells. The first cell (Core 1) is located in the southeastern corner of Madagascar, and the second cell (Core 2) is west of the southern tip of Madagascar. These two cores are characterized by different seasonal variability, different intensities, different upwelled water mass origins, and distinct forcing mechanisms. Core 1 is associated with a dynamical upwelling forced by the detachment of the East Madagascar Current (EMC), which is reinforced by upwelling favourable winds. Core 2 appears to be primarily forced by upwelling favourable winds, but is also influenced by a poleward eastern boundary flow coming from the Mozambique Channel. The intrusion of Mozambique Channel warm waters could result in an asynchronicity in seasonality between upwelling surface signature and upwelling favourables winds.

Interannual and spatial variability of maple syrup yield as related to climatic factors

Science.gov (United States)

Houle, Daniel

2014-01-01

Sugar maple syrup production is an important economic activity for eastern Canada and the northeastern United States. Since annual variations in syrup yield have been related to climate, there are concerns about the impacts of climatic change on the industry in the upcoming decades. Although the temporal variability of syrup yield has been studied for specific sites on different time scales or for large regions, a model capable of accounting for both temporal and regional differences in yield is still lacking. In the present study, we studied the factors responsible for interregional and interannual variability in maple syrup yield over the 2001–2012 period, by combining the data from 8 Quebec regions (Canada) and 10 U.S. states. The resulting model explained 44.5% of the variability in yield. It includes the effect of climatic conditions that precede the sapflow season (variables from the previous growing season and winter), the effect of climatic conditions during the current sapflow season, and terms accounting for intercountry and temporal variability. Optimal conditions for maple syrup production appear to be spatially restricted by less favourable climate conditions occurring during the growing season in the north, and in the south, by the warmer winter and earlier spring conditions. This suggests that climate change may favor maple syrup production northwards, while southern regions are more likely to be negatively affected by adverse spring conditions. PMID:24949244

Spatial variability of soil carbon stock in the Urucu river basin, Central Amazon-Brazil.

Science.gov (United States)

Ceddia, Marcos Bacis; Villela, André Luis Oliveira; Pinheiro, Érika Flávia Machado; Wendroth, Ole

2015-09-01

The Amazon Forest plays a major role in C sequestration and release. However, few regional estimates of soil organic carbon (SOC) stock in this ecoregion exist. One of the barriers to improve SOC estimates is the lack of recent soil data at high spatial resolution, which hampers the application of new methods for mapping SOC stock. The aims of this work were: (i) to quantify SOC stock under undisturbed vegetation for the 0-30 and the 0-100 cm under Amazon Forest; (ii) to correlate the SOC stock with soil mapping units and relief attributes and (iii) to evaluate three geostatistical techniques to generate maps of SOC stock (ordinary, isotopic and heterotopic cokriging). The study site is located in the Central region of Amazon State, Brazil. The soil survey covered the study site that has an area of 80 km(2) and resulted in a 1:10,000 soil map. It consisted of 315 field observations (96 complete soil profiles and 219 boreholes). SOC stock was calculated by summing C stocks by horizon, determined as a product of BD, SOC and the horizon thickness. For each one of the 315 soil observations, relief attributes were derived from a topographic map to understand SOC dynamics. The SOC stocks across 30 and 100 cm soil depth were 3.28 and 7.32 kg C m(-2), respectively, which is, 34 and 16%, lower than other studies. The SOC stock is higher in soils developed in relief forms exhibiting well-drained soils, which are covered by Upland Dense Tropical Rainforest. Only SOC stock in the upper 100 cm exhibited spatial dependence allowing the generation of spatial variability maps based on spatial (co)-regionalization. The CTI was inversely correlated with SOC stock and was the only auxiliary variable feasible to be used in cokriging interpolation. The heterotopic cokriging presented the best performance for mapping SOC stock. Copyright © 2015 Elsevier B.V. All rights reserved.

Robust Exponential Synchronization for a Class of Master-Slave Distributed Parameter Systems with Spatially Variable Coefficients and Nonlinear Perturbation

Directory of Open Access Journals (Sweden)

Chengdong Yang

2015-01-01

Full Text Available This paper addresses the exponential synchronization problem of a class of master-slave distributed parameter systems (DPSs with spatially variable coefficients and spatiotemporally variable nonlinear perturbation, modeled by a couple of semilinear parabolic partial differential equations (PDEs. With a locally Lipschitz constraint, the perturbation is a continuous function of time, space, and system state. Firstly, a sufficient condition for the robust exponential synchronization of the unforced semilinear master-slave PDE systems is investigated for all admissible nonlinear perturbations. Secondly, a robust distributed proportional-spatial derivative (P-sD state feedback controller is desired such that the closed-loop master-slave PDE systems achieve exponential synchronization. Using Lyapunov’s direct method and the technique of integration by parts, the main results of this paper are presented in terms of spatial differential linear matrix inequalities (SDLMIs. Finally, two numerical examples are provided to show the effectiveness of the proposed methods applied to the robust exponential synchronization problem of master-slave PDE systems with nonlinear perturbation.

Structured Spatial Modeling and Mapping of Domestic Violence Against Women of Reproductive Age in Rwanda.

Science.gov (United States)

Habyarimana, Faustin; Zewotir, Temesgen; Ramroop, Shaun

2018-03-01

The main objective of this study was to assess the risk factors and spatial correlates of domestic violence against women of reproductive age in Rwanda. A structured spatial approach was used to account for the nonlinear nature of some covariates and the spatial variability on domestic violence. The nonlinear effect was modeled through second-order random walk, and the structured spatial effect was modeled through Gaussian Markov Random Fields specified as an intrinsic conditional autoregressive model. The data from the Rwanda Demographic and Health Survey 2014/2015 were used as an application. The findings of this study revealed that the risk factors of domestic violence against women are the wealth quintile of the household, the size of the household, the husband or partner's age, the husband or partner's level of education, ownership of the house, polygamy, the alcohol consumption status of the husband or partner, the woman's perception of wife-beating attitude, and the use of contraceptive methods. The study also highlighted the significant spatial variation of domestic violence against women at district level.

Scale-dependent spatial variability in peatland lead pollution in the southern Pennines, UK

International Nuclear Information System (INIS)

Rothwell, James J.; Evans, Martin G.; Lindsay, John B.; Allott, Timothy E.H.

2007-01-01

Increasingly, within-site and regional comparisons of peatland lead pollution have been undertaken using the inventory approach. The peatlands of the Peak District, southern Pennines, UK, have received significant atmospheric inputs of lead over the last few hundred years. A multi-core study at three peatland sites in the Peak District demonstrates significant within-site spatial variability in industrial lead pollution. Stochastic simulations reveal that 15 peat cores are required to calculate reliable lead inventories at the within-site and within-region scale for this highly polluted area of the southern Pennines. Within-site variability in lead pollution is dominant at the within-region scale. The study demonstrates that significant errors may be associated with peatland lead inventories at sites where only a single peat core has been used to calculate an inventory. Meaningful comparisons of lead inventories at the regional or global scale can only be made if the within-site variability of lead pollution has been quantified reliably. - Multiple peat cores are required for accurate peatland Pb inventories

Examining impulse-variability in overarm throwing.

Science.gov (United States)

Urbin, M A; Stodden, David; Boros, Rhonda; Shannon, David

2012-01-01

The purpose of this study was to examine variability in overarm throwing velocity and spatial output error at various percentages of maximum to test the prediction of an inverted-U function as predicted by impulse-variability theory and a speed-accuracy trade-off as predicted by Fitts' Law Thirty subjects (16 skilled, 14 unskilled) were instructed to throw a tennis ball at seven percentages of their maximum velocity (40-100%) in random order (9 trials per condition) at a target 30 feet away. Throwing velocity was measured with a radar gun and interpreted as an index of overall systemic power output. Within-subject throwing velocity variability was examined using within-subjects repeated-measures ANOVAs (7 repeated conditions) with built-in polynomial contrasts. Spatial error was analyzed using mixed model regression. Results indicated a quadratic fit with variability in throwing velocity increasing from 40% up to 60%, where it peaked, and then decreasing at each subsequent interval to maximum (p < .001, η2 = .555). There was no linear relationship between speed and accuracy. Overall, these data support the notion of an inverted-U function in overarm throwing velocity variability as both skilled and unskilled subjects approach maximum effort. However, these data do not support the notion of a speed-accuracy trade-off. The consistent demonstration of an inverted-U function associated with systemic power output variability indicates an enhanced capability to regulate aspects of force production and relative timing between segments as individuals approach maximum effort, even in a complex ballistic skill.

Spatial representation and cognitive modulation of response variability in the lateral intraparietal area priority map.

Science.gov (United States)

Falkner, Annegret L; Goldberg, Michael E; Krishna, B Suresh

2013-10-09

The lateral intraparietal area (LIP) in the macaque contains a priority-based representation of the visual scene. We previously showed that the mean spike rate of LIP neurons is strongly influenced by spatially wide-ranging surround suppression in a manner that effectively sharpens the priority map. Reducing response variability can also improve the precision of LIP's priority map. We show that when a monkey plans a visually guided delayed saccade with an intervening distractor, variability (measured by the Fano factor) decreases both for neurons representing the saccade goal and for neurons representing the broad spatial surround. The reduction in Fano factor is maximal for neurons representing the saccade goal and steadily decreases for neurons representing more distant locations. LIP Fano factor changes are behaviorally significant: increasing expected reward leads to lower variability for the LIP representation of both the target and distractor locations, and trials with shorter latency saccades are associated with lower Fano factors in neurons representing the surround. Thus, the LIP Fano factor reflects both stimulus and behavioral engagement. Quantitative modeling shows that the interaction between mean spike count and target-receptive field (RF) distance in the surround during the predistractor epoch is multiplicative: the Fano factor increases more steeply with mean spike count further away from the RF. A negative-binomial model for LIP spike counts captures these findings quantitatively, suggests underlying mechanisms based on trial-by-trial variations in mean spike rate or burst-firing patterns, and potentially provides a principled framework to account simultaneously for the previously observed unsystematic relationships between spike rate and variability in different brain areas.

A new reliability measure based on specified minimum distances before the locations of random variables in a finite interval

International Nuclear Information System (INIS)

Todinov, M.T.

2004-01-01

A new reliability measure is proposed and equations are derived which determine the probability of existence of a specified set of minimum gaps between random variables following a homogeneous Poisson process in a finite interval. Using the derived equations, a method is proposed for specifying the upper bound of the random variables' number density which guarantees that the probability of clustering of two or more random variables in a finite interval remains below a maximum acceptable level. It is demonstrated that even for moderate number densities the probability of clustering is substantial and should not be neglected in reliability calculations. In the important special case where the random variables are failure times, models have been proposed for determining the upper bound of the hazard rate which guarantees a set of minimum failure-free operating intervals before the random failures, with a specified probability. A model has also been proposed for determining the upper bound of the hazard rate which guarantees a minimum availability target. Using the models proposed, a new strategy, models and reliability tools have been developed for setting quantitative reliability requirements which consist of determining the intersection of the hazard rate envelopes (hazard rate upper bounds) which deliver a minimum failure-free operating period before random failures, a risk of premature failure below a maximum acceptable level and a minimum required availability. It is demonstrated that setting reliability requirements solely based on an availability target does not necessarily mean a low risk of premature failure. Even at a high availability level, the probability of premature failure can be substantial. For industries characterised by a high cost of failure, the reliability requirements should involve a hazard rate envelope limiting the risk of failure below a maximum acceptable level

Characterization factors for terrestrial acidification at the global scale: a systematic analysis of spatial variability and uncertainty.

Science.gov (United States)

Roy, Pierre-Olivier; Azevedo, Ligia B; Margni, Manuele; van Zelm, Rosalie; Deschênes, Louise; Huijbregts, Mark A J

2014-12-01

Characterization factors (CFs) are used in life cycle assessment (LCA) to quantify the potential impact per unit of emission. CFs are obtained from a characterization model which assess the environmental mechanisms along the cause-effect chain linking an emission to its potential damage on a given area of protection, such as loss in ecosystem quality. Up to now, CFs for acidifying emissions did not cover the global scale and were only representative of their characterization model geographical scope. Consequently, current LCA practices implicitly assume that all emissions from a global supply chain occur within the continent referring to the characterization method geographical scope. This paper provides worldwide 2°×2.5° spatially-explicit CFs, representing the change in relative loss of terrestrial vascular plant species due to an emission change of nitrogen oxides (NOx), ammonia (NH3) and sulfur dioxide (SO2). We found that spatial variability in the CFs is much larger compared to statistical uncertainty (six orders of magnitude vs. two orders of magnitude). Spatial variability is mainly caused by the atmospheric fate factor and soil sensitivity factor, while the ecological effect factor is the dominant contributor to the statistical uncertainty. The CFs provided in our study allow the worldwide spatially explicit evaluation of life cycle impacts related to acidifying emissions. This opens the door to evaluate regional life cycle emissions of different products in a global economy. Copyright © 2014 Elsevier B.V. All rights reserved.

Equivalent conditions of complete moment convergence for extended negatively dependent random variables

Directory of Open Access Journals (Sweden)

Qunying Wu

2017-05-01

Full Text Available Abstract In this paper, we study the equivalent conditions of complete moment convergence for sequences of identically distributed extended negatively dependent random variables. As a result, we extend and generalize some results of complete moment convergence obtained by Chow (Bull. Inst. Math. Acad. Sin. 16:177-201, 1988 and Li and Spătaru (J. Theor. Probab. 18:933-947, 2005 from the i.i.d. case to extended negatively dependent sequences.

Spatial analysis of factors influencing long-term stress in the grizzly bear (Ursus arctos population of Alberta, Canada.

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Mathieu L Bourbonnais

Full Text Available Non-invasive measures for assessing long-term stress in free ranging mammals are an increasingly important approach for understanding physiological responses to landscape conditions. Using a spatially and temporally expansive dataset of hair cortisol concentrations (HCC generated from a threatened grizzly bear (Ursus arctos population in Alberta, Canada, we quantified how variables representing habitat conditions and anthropogenic disturbance impact long-term stress in grizzly bears. We characterized spatial variability in male and female HCC point data using kernel density estimation and quantified variable influence on spatial patterns of male and female HCC stress surfaces using random forests. Separate models were developed for regions inside and outside of parks and protected areas to account for substantial differences in anthropogenic activity and disturbance within the study area. Variance explained in the random forest models ranged from 55.34% to 74.96% for males and 58.15% to 68.46% for females. Predicted HCC levels were higher for females compared to males. Generally, high spatially continuous female HCC levels were associated with parks and protected areas while low-to-moderate levels were associated with increased anthropogenic disturbance. In contrast, male HCC levels were low in parks and protected areas and low-to-moderate in areas with increased anthropogenic disturbance. Spatial variability in gender-specific HCC levels reveal that the type and intensity of external stressors are not uniform across the landscape and that male and female grizzly bears may be exposed to, or perceive, potential stressors differently. We suggest observed spatial patterns of long-term stress may be the result of the availability and distribution of foods related to disturbance features, potential sexual segregation in available habitat selection, and may not be influenced by sources of mortality which represent acute traumas. In this wildlife

Multi-index Stochastic Collocation Convergence Rates for Random PDEs with Parametric Regularity

KAUST Repository

Haji Ali, Abdul Lateef; Nobile, Fabio; Tamellini, Lorenzo; Tempone, Raul

2016-01-01

We analyze the recent Multi-index Stochastic Collocation (MISC) method for computing statistics of the solution of a partial differential equation (PDE) with random data, where the random coefficient is parametrized by means of a countable sequence of terms in a suitable expansion. MISC is a combination technique based on mixed differences of spatial approximations and quadratures over the space of random data, and naturally, the error analysis uses the joint regularity of the solution with respect to both the variables in the physical domain and parametric variables. In MISC, the number of problem solutions performed at each discretization level is not determined by balancing the spatial and stochastic components of the error, but rather by suitably extending the knapsack-problem approach employed in the construction of the quasi-optimal sparse-grids and Multi-index Monte Carlo methods, i.e., we use a greedy optimization procedure to select the most effective mixed differences to include in the MISC estimator. We apply our theoretical estimates to a linear elliptic PDE in which the log-diffusion coefficient is modeled as a random field, with a covariance similar to a Matérn model, whose realizations have spatial regularity determined by a scalar parameter. We conduct a complexity analysis based on a summability argument showing algebraic rates of convergence with respect to the overall computational work. The rate of convergence depends on the smoothness parameter, the physical dimensionality and the efficiency of the linear solver. Numerical experiments show the effectiveness of MISC in this infinite dimensional setting compared with the Multi-index Monte Carlo method and compare the convergence rate against the rates predicted in our theoretical analysis. © 2016 SFoCM

Multi-index Stochastic Collocation Convergence Rates for Random PDEs with Parametric Regularity

KAUST Repository

Haji Ali, Abdul Lateef

2016-08-26

We analyze the recent Multi-index Stochastic Collocation (MISC) method for computing statistics of the solution of a partial differential equation (PDE) with random data, where the random coefficient is parametrized by means of a countable sequence of terms in a suitable expansion. MISC is a combination technique based on mixed differences of spatial approximations and quadratures over the space of random data, and naturally, the error analysis uses the joint regularity of the solution with respect to both the variables in the physical domain and parametric variables. In MISC, the number of problem solutions performed at each discretization level is not determined by balancing the spatial and stochastic components of the error, but rather by suitably extending the knapsack-problem approach employed in the construction of the quasi-optimal sparse-grids and Multi-index Monte Carlo methods, i.e., we use a greedy optimization procedure to select the most effective mixed differences to include in the MISC estimator. We apply our theoretical estimates to a linear elliptic PDE in which the log-diffusion coefficient is modeled as a random field, with a covariance similar to a Matérn model, whose realizations have spatial regularity determined by a scalar parameter. We conduct a complexity analysis based on a summability argument showing algebraic rates of convergence with respect to the overall computational work. The rate of convergence depends on the smoothness parameter, the physical dimensionality and the efficiency of the linear solver. Numerical experiments show the effectiveness of MISC in this infinite dimensional setting compared with the Multi-index Monte Carlo method and compare the convergence rate against the rates predicted in our theoretical analysis. © 2016 SFoCM

Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology

Science.gov (United States)

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological datasets there is limited guidance on variable selection methods for RF modeling. Typically, e...

Generating Correlated QPSK Waveforms By Exploiting Real Gaussian Random Variables

KAUST Repository

Jardak, Seifallah

2012-11-01

The design of waveforms with specified auto- and cross-correlation properties has a number of applications in multiple-input multiple-output (MIMO) radar, one of them is the desired transmit beampattern design. In this work, an algorithm is proposed to generate quadrature phase shift- keying (QPSK) waveforms with required cross-correlation properties using real Gaussian random-variables (RV’s). This work can be considered as the extension of what was presented in [1] to generate BPSK waveforms. This work will be extended for the generation of correlated higher-order phase shift-keying (PSK) and quadrature amplitude modulation (QAM) schemes that can better approximate the desired beampattern.

Generating Correlated QPSK Waveforms By Exploiting Real Gaussian Random Variables

KAUST Repository

Jardak, Seifallah; Ahmed, Sajid; Alouini, Mohamed-Slim

2012-01-01

The design of waveforms with specified auto- and cross-correlation properties has a number of applications in multiple-input multiple-output (MIMO) radar, one of them is the desired transmit beampattern design. In this work, an algorithm is proposed to generate quadrature phase shift- keying (QPSK) waveforms with required cross-correlation properties using real Gaussian random-variables (RV’s). This work can be considered as the extension of what was presented in [1] to generate BPSK waveforms. This work will be extended for the generation of correlated higher-order phase shift-keying (PSK) and quadrature amplitude modulation (QAM) schemes that can better approximate the desired beampattern.

Spatial and Temporal Variability and Trends in 2001-2016 Global Fire Activity

Science.gov (United States)

Earl, Nick; Simmonds, Ian

2018-03-01

Fire regimes across the globe have great spatial and temporal variability, and these are influence by many factors including anthropogenic management, climate, and vegetation types. Here we utilize the satellite-based "active fire" product, from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, to statistically analyze variability and trends in fire activity from the global to regional scales. We split up the regions by economic development, region/geographical land use, clusters of fire-abundant areas, or by religious/cultural influence. Weekly cycle tests are conducted to highlight and quantify part of the anthropogenic influence on fire regime across the world. We find that there is a strong statistically significant decline in 2001-2016 active fires globally linked to an increase in net primary productivity observed in northern Africa, along with global agricultural expansion and intensification, which generally reduces fire activity. There are high levels of variability, however. The large-scale regions exhibit either little change or decreasing in fire activity except for strong increasing trends in India and China, where rapid population increase is occurring, leading to agricultural intensification and increased crop residue burning. Variability in Canada has been linked to a warming global climate leading to a longer growing season and higher fuel loads. Areas with a strong weekly cycle give a good indication of where fire management is being applied most extensively, for example, the United States, where few areas retain a natural fire regime.

LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

Science.gov (United States)

Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.

2015-01-01

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.

Spatially uniform but temporally variable bacterioplankton in a semi-enclosed coastal area.

Science.gov (United States)

Meziti, Alexandra; Kormas, Konstantinos A; Moustaka-Gouni, Maria; Karayanni, Hera

2015-07-01

Studies focusing on the temporal and spatial dynamics of bacterioplankton communities within littoral areas undergoing direct influences from the coast are quite limited. In addition, they are more complicated to resolve compared to communities in the open ocean. In order to elucidate the effects of spatial vs. temporal variability on bacterial communities in a highly land-influenced semi-enclosed gulf, surface bacterioplankton communities from five coastal sites in Igoumenitsa Gulf (Ionian Sea, Greece) were analyzed over a nine-month period using 16S rDNA 454-pyrosequencing. Temporal differences were more pronounced than spatial ones, with lower diversity indices observed during the summer months. During winter and early spring, bacterial communities were dominated by SAR11 representatives, while this pattern changed in May when they were abruptly replaced by members of Flavobacteriales, Pseudomonadales, and Alteromonadales. Additionally, correlation analysis showed high negative correlations between the presence of SAR11 OTUs in relation to temperature and sunlight that might have driven, directly or indirectly, the disappearance of these OTUs in the summer months. The dominance of SAR11 during the winter months further supported the global distribution of the clade, not only in the open-sea, but also in coastal systems. This study revealed that specific bacteria exhibited distinct succession patterns in an anthropogenic-impacted coastal system. The major bacterioplankton component was represented by commonly found marine bacteria exhibiting seasonal dynamics, while freshwater and terrestrial-related phylotypes were absent. Copyright © 2015 Elsevier GmbH. All rights reserved.

Temporal and spatial variabilities of Antarctic ice mass changes inferred by GRACE in a Bayesian framework

Science.gov (United States)

Wang, L.; Davis, J. L.; Tamisiea, M. E.

2017-12-01

The Antarctic ice sheet (AIS) holds about 60% of all fresh water on the Earth, an amount equivalent to about 58 m of sea-level rise. Observation of AIS mass change is thus essential in determining and predicting its contribution to sea level. While the ice mass loss estimates for West Antarctica (WA) and the Antarctic Peninsula (AP) are in good agreement, what the mass balance over East Antarctica (EA) is, and whether or not it compensates for the mass loss is under debate. Besides the different error sources and sensitivities of different measurement types, complex spatial and temporal variabilities would be another factor complicating the accurate estimation of the AIS mass balance. Therefore, a model that allows for variabilities in both melting rate and seasonal signals would seem appropriate in the estimation of present-day AIS melting. We present a stochastic filter technique, which enables the Bayesian separation of the systematic stripe noise and mass signal in decade-length GRACE monthly gravity series, and allows the estimation of time-variable seasonal and inter-annual components in the signals. One of the primary advantages of this Bayesian method is that it yields statistically rigorous uncertainty estimates reflecting the inherent spatial resolution of the data. By applying the stochastic filter to the decade-long GRACE observations, we present the temporal variabilities of the AIS mass balance at basin scale, particularly over East Antarctica, and decipher the EA mass variations in the past decade, and their role in affecting overall AIS mass balance and sea level.

Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data

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Daniel A. Griffith

2016-06-01

Full Text Available Virtually all remotely sensed data contain spatial autocorrelation, which impacts upon their statistical features of uncertainty through variance inflation, and the compounding of duplicate information. Estimating the nature and degree of this spatial autocorrelation, which is usually positive and very strong, has been hindered by computational intensity associated with the massive number of pixels in realistically-sized remotely-sensed images, a situation that more recently has changed. Recent advances in spatial statistical estimation theory support the extraction of information and the distilling of knowledge from remotely-sensed images in a way that accounts for latent spatial autocorrelation. This paper summarizes an effective methodological approach to achieve this end, illustrating results with a 2002 remotely sensed-image of the Florida Everglades, and simulation experiments. Specifically, uncertainty of spatial autocorrelation parameter in a spatial autoregressive model is modeled with a beta-beta mixture approach and is further investigated with three different sampling strategies: coterminous sampling, random sub-region sampling, and increasing domain sub-regions. The results suggest that uncertainty associated with remotely-sensed data should be cast in consideration of spatial autocorrelation. It emphasizes that one remaining challenge is to better quantify the spatial variability of spatial autocorrelation estimates across geographic landscapes.

Spontaneous temporal changes and variability of peripheral nerve conduction analyzed using a random effects model

DEFF Research Database (Denmark)

Krøigård, Thomas; Gaist, David; Otto, Marit

2014-01-01

SUMMARY: The reproducibility of variables commonly included in studies of peripheral nerve conduction in healthy individuals has not previously been analyzed using a random effects regression model. We examined the temporal changes and variability of standard nerve conduction measures in the leg...... reexamined after 2 and 26 weeks. There was no change in the variables except for a minor decrease in sural nerve sensory action potential amplitude and a minor increase in tibial nerve minimal F-wave latency. Reproducibility was best for peroneal nerve distal motor latency and motor conduction velocity......, sural nerve sensory conduction velocity, and tibial nerve minimal F-wave latency. Between-subject variability was greater than within-subject variability. Sample sizes ranging from 21 to 128 would be required to show changes twice the magnitude of the spontaneous changes observed in this study. Nerve...

Automatic Probabilistic Program Verification through Random Variable Abstraction

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Damián Barsotti

2010-06-01

Full Text Available The weakest pre-expectation calculus has been proved to be a mature theory to analyze quantitative properties of probabilistic and nondeterministic programs. We present an automatic method for proving quantitative linear properties on any denumerable state space using iterative backwards fixed point calculation in the general framework of abstract interpretation. In order to accomplish this task we present the technique of random variable abstraction (RVA and we also postulate a sufficient condition to achieve exact fixed point computation in the abstract domain. The feasibility of our approach is shown with two examples, one obtaining the expected running time of a probabilistic program, and the other the expected gain of a gambling strategy. Our method works on general guarded probabilistic and nondeterministic transition systems instead of plain pGCL programs, allowing us to easily model a wide range of systems including distributed ones and unstructured programs. We present the operational and weakest precondition semantics for this programs and prove its equivalence.

Assessing variability in Orbiting Carbon Observatory-2 (OCO-2) XCO2 using high spatial resolution color slices and other retrieval parameters

Science.gov (United States)

Merrelli, A. J.; Taylor, T.; O'Dell, C.; Cronk, H. Q.; Eldering, A.; Crisp, D.

2017-12-01

The Orbiting Carbon Observatory-2 (OCO-2) measures reflected sunlight in the Oxygen A-band (0.76 μm), Weak CO2 band (1.61 μm) and Strong CO2 band (2.06 μm) with resolving powers 18,000, 19,500 and 19,500, respectively. Soundings are collected at 3Hz, yielding 8 contiguous cities, the variability of XCO2 over small scales, e.g., tens of kilometers, is expected to be less than 1 ppm. However, deviations on the order of +/- 2 ppm, or more, are often observed in the production Version 7 (B7) data product. We hypothesize that most of this variability is spurious, with contributions from both retrieval errors and undetected cloud and aerosol contamination. The contiguous nature of the OCO-2 spatial sampling allows for analysis of the variability in XCO2 and correlation with variables, such as the full spatial resolution "color slices" and other retrieved parameters. Color slices avoid the on-board averaging across the detector focal plane array, providing increased spatial information compared to the nominal spectra. This work explores the new B8 production data set using MODIS visible imagery from the CSU Vistool to provide visual context to the OCO-2 parameters. The large volume of data that has been collected since September 2014 allows for statistical analysis of parameters in relation to XCO2 variability. Some detailed case studies are presented.

Research into the influence of spatial variability and scale on the parameterization of hydrological processes

Science.gov (United States)

Wood, Eric F.

1993-01-01

The objectives of the research were as follows: (1) Extend the Representative Elementary Area (RE) concept, first proposed and developed in Wood et al, (1988), to the water balance fluxes of the interstorm period (redistribution, evapotranspiration and baseflow) necessary for the analysis of long-term water balance processes. (2) Derive spatially averaged water balance model equations for spatially variable soil, topography and vegetation, over A RANGE OF CLIMATES. This is a necessary step in our goal to derive consistent hydrologic results up to GCM grid scales necessary for global climate modeling. (3) Apply the above macroscale water balance equations with remotely sensed data and begin to explore the feasibility of parameterizing the water balance constitutive equations at GCM grid scale.

Fine scale spatial variability of microbial pesticide degradation in soil: scales, controlling factors, and implications

DEFF Research Database (Denmark)

Dechesne, Arnaud; Badawi, N.; Aamand, Jens

2014-01-01

across pesticide classes: they include some soil characteristics (pH) and some agricultural management practices (pesticide application, tillage), while other potential controlling factors have more conflicting effects depending on the site or the pesticide. Evidence demonstrating the importance......Pesticide biodegradation is a soil microbial function of critical importance for modern agriculture and its environmental impact. While it was once assumed that this activity was homogeneously distributed at the field scale, mounting evidence indicates that this is rarely the case. Here, we...... critically examine the literature on spatial variability of pesticide biodegradation in agricultural soil. We discuss the motivations, methods, and main findings of the primary literature. We found significant diversity in the approaches used to describe and quantify spatial heterogeneity, which complicates...

High spatial variability in biogeochemical rates and microbial communities across Louisiana salt marsh landscapes

Science.gov (United States)

Roberts, B. J.; Chelsky, A.; Bernhard, A. E.; Giblin, A. E.

2017-12-01

Salt marshes are important sites for retention and transformation of carbon and nutrients. Much of our current marsh biogeochemistry knowledge is based on sampling at times and in locations that are convenient, most often vegetated marsh platforms during low tide. Wetland loss rates are high in many coastal regions including Louisiana which has the highest loss rates in the US. This loss not only reduces total marsh area but also changes the relative allocation of subhabitats in the remaining marsh. Climate and other anthropogenic changes lead to further changes including inundation patterns, redox conditions, salinity regimes, and shifts in vegetation patterns across marsh landscapes. We present results from a series of studies examining biogeochemical rates, microbial communities, and soil properties along multiple edge to interior transects within Spartina alterniflora across the Louisiana coast; between expanding patches of Avicennia germinans and adjacent S. alterniflora marshes; in soils associated with the four most common Louisiana salt marsh plants species; and across six different marsh subhabitats. Spartina alterniflora marsh biogeochemistry and microbial populations display high spatial variability related to variability in soil properties which appear to be, at least in part, regulated by differences in elevation, hydrology, and redox conditions. Differences in rates between soils associated with different vegetation types were also related to soil properties with S. alterniflora soils often yielding the lowest rates. Biogeochemical process rates vary significantly across marsh subhabitats with individual process rates differing in their hotspot habitat(s) across the marsh. Distinct spatial patterns may influence the roles that marshes play in retaining and transforming nutrients in coastal regions and highlight the importance of incorporating spatial sampling when scaling up plot level measurements to landscape or regional scales.

Performance of some biotic indices in the real variable world: A case study at different spatial scales in North-Western Mediterranean Sea

International Nuclear Information System (INIS)

Tataranni, Mariella; Lardicci, Claudio

2010-01-01

The aim of this study was to analyse the variability of four different benthic biotic indices (AMBI, BENTIX, H', M-AMBI) in two marine coastal areas of the North-Western Mediterranean Sea. In each coastal area, 36 replicates were randomly selected according to a hierarchical sampling design, which allowed estimating the variance components of the indices associated with four different spatial scales (ranging from metres to kilometres). All the analyses were performed at two different sampling periods in order to evaluate if the observed trends were consistent over the time. The variance components of the four indices revealed complex trends and different patterns in the two sampling periods. These results highlighted that independently from the employed index, a rigorous and appropriate sampling design taking into account different scales should always be used in order to avoid erroneous classifications and to develop effective monitoring programs. - How heterogeneous distribution of macrobenthos can affect the performance of some biotic indices.

The spatial variable glacier mass loss over the southeast Tibet Plateau and the climate cause analyses

Science.gov (United States)

Ke, L.; Ding, X.; Song, C.; Sheng, Y.

2016-12-01

Temperate glaciers can be highly sensitive to global climate change due to relatively humid and warm local climate. Numerous temperate glaciers are distributed in the southeastern Tibet Plateau (SETP) and their changes are still poorly represented. Based on a latest glacier inventory and ICESat altimetry measurements, we examine the spatial heterogeneity of glacier change in the SETP (including the central and eastern Nyainqêntanglha ranges) and further analyze its relation with climate change by using station-based and gridded meteorological data. Our results show that SETP glaciers experienced drastic surface lowering at about -0.84±0.26 m a-1 on average over 2003-2008. Debris-covered ice thinned at an average rate of -1.13±0.32 m a-1, in comparison with -0.92±0.17 m a-1 over the debris-free ice areas. The thinning rate is the strongest in the southeastern sub-region (up to -1.24 m a-1 ) and moderate ( -0.45 m a-1 ) in the central and northwestern parts, which is in general agreement with the pattern of surface mass changes based on the GRACE gravimetry observation. Long-term climate data at weather stations show that, in comparison with the period of 1992-2002, mean temperature increased by 0.46 °C - 0.59 °C in the recent decade (2003-2013); while the change of summer precipitation exhibited remarkably spatial variability, following a southeast-northwest contrasting pattern (decreasing by over 10% in the southeast, to stable level in the central region, and increment up to 10% in the northwest). This spatially variable precipitation change is consistent with results from CN05 grid data and ERA re-analysis data, and agrees well with the spatial pattern of glacier surface elevation changes. The results suggest that overall negative glacier mass balances in SETP are governed by temperature rising, while the different precipitation change could contribute to inconsistent glacier thinning rates. The spatial pattern of precipitation decrease and mass loss might

The Distribution of Minimum of Ratios of Two Random Variables and Its Application in Analysis of Multi-hop Systems

Directory of Open Access Journals (Sweden)

A. Stankovic

2012-12-01

Full Text Available The distributions of random variables are of interest in many areas of science. In this paper, ascertaining on the importance of multi-hop transmission in contemporary wireless communications systems operating over fading channels in the presence of cochannel interference, the probability density functions (PDFs of minimum of arbitrary number of ratios of Rayleigh, Rician, Nakagami-m, Weibull and α-µ random variables are derived. These expressions can be used to study the outage probability as an important multi-hop system performance measure. Various numerical results complement the proposed mathematical analysis.

The Spatial and Temporal Variability of the North Atlantic Oscillation Recorded in Ice Core Major Ion Time Series

Science.gov (United States)

Wawrzeniak, T. L.; Wake, C. P.; Fischer, H.; Fisher, D. A.; Schwikowski, M.

2006-05-01

The North Atlantic Oscillation represents a significant mode of atmospheric variability for the Arctic and sub- Artic climate system. Developing a longer-term record of the spatial and temporal variability of the NAO could improve our understanding of natural climate variability in the region. Previous work has shown a significant relationship between Greenland ice core records and the NAO. Here, we have compared sea-salt and dust records from nine ice cores around the Arctic region to sea level pressure and NAO indices to evaluate the extent to which these ice cores can be used to reconstruct the NAO.

Providing a non-deterministic representation of spatial variability of precipitation in the Everest region

Directory of Open Access Journals (Sweden)

J. Eeckman

2017-09-01

Full Text Available This paper provides a new representation of the effect of altitude on precipitation that represents spatial and temporal variability in precipitation in the Everest region. Exclusive observation data are used to infer a piecewise linear function for the relation between altitude and precipitation and significant seasonal variations are highlighted. An original ensemble approach is applied to provide non-deterministic water budgets for middle and high-mountain catchments. Physical processes at the soil–atmosphere interface are represented through the Interactions Soil–Biosphere–Atmosphere (ISBA surface scheme. Uncertainties associated with the model parametrization are limited by the integration of in situ measurements of soils and vegetation properties. Uncertainties associated with the representation of the orographic effect are shown to account for up to 16 % of annual total precipitation. Annual evapotranspiration is shown to represent 26 % ± 1 % of annual total precipitation for the mid-altitude catchment and 34% ± 3 % for the high-altitude catchment. Snowfall contribution is shown to be neglectable for the mid-altitude catchment, and it represents up to 44 % ± 8 % of total precipitation for the high-altitude catchment. These simulations on the local scale enhance current knowledge of the spatial variability in hydroclimatic processes in high- and mid-altitude mountain environments.

The AIDS epidemic in the Amazon region: a spatial case-control study in Rondonia, Brazil

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Maria Rita Donalisio

2013-10-01

Full Text Available OBJECTIVE To analyze spatial changes in the risk of AIDS and the relationship between AIDS incidence and socioeconomic variables in the state of Rondonia, Amazon region. METHODS A spatial, population case-control study in Rondonia, Brazil, based on 1,780 cases reported to the Epidemiological Surveillance System and controls based on demographic data from 1987 to 2006. The cases were grouped into five consecutive four-year periods. A generalized additive model was adjusted to the data; the dependent variable was the status of the individuals (case or control, and the independent variables were a bi-dimensional spline of the geographic coordinates and some municipality-level socioeconomic variables. The observed values of the Moran’s I test were compared to a reference distribution of values generated under conditions of spatial randomness. RESULTS AIDS risk shows a marked spatial and temporal pattern. The disease incidence is related to socioeconomic variables at the municipal level in Rondônia, such as urbanization and human capital. The highest incidence rates of AIDS are in municipalities along the BR-364 highway and calculations of the Moran’s I test show positive spatial correlation associated with proximity of the municipality to the highway in the third and fourth periods (p = 0.05. CONCLUSIONS Incidence of the disease is higher in municipalities of greater economic wealth and urbanization, and in those municipalities bisected by Rondônia’s main roads. The rapid development associated with the opening up of once remote regions may be accompanied by an increase in these risks to health.

Wave speed in excitable random networks with spatially constrained connections.

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Nikita Vladimirov

Full Text Available Very fast oscillations (VFO in neocortex are widely observed before epileptic seizures, and there is growing evidence that they are caused by networks of pyramidal neurons connected by gap junctions between their axons. We are motivated by the spatio-temporal waves of activity recorded using electrocorticography (ECoG, and study the speed of activity propagation through a network of neurons axonally coupled by gap junctions. We simulate wave propagation by excitable cellular automata (CA on random (Erdös-Rényi networks of special type, with spatially constrained connections. From the cellular automaton model, we derive a mean field theory to predict wave propagation. The governing equation resolved by the Fisher-Kolmogorov PDE fails to describe wave speed. A new (hyperbolic PDE is suggested, which provides adequate wave speed v( that saturates with network degree , in agreement with intuitive expectations and CA simulations. We further show that the maximum length of connection is a much better predictor of the wave speed than the mean length. When tested in networks with various degree distributions, wave speeds are found to strongly depend on the ratio of network moments / rather than on mean degree , which is explained by general network theory. The wave speeds are strikingly similar in a diverse set of networks, including regular, Poisson, exponential and power law distributions, supporting our theory for various network topologies. Our results suggest practical predictions for networks of electrically coupled neurons, and our mean field method can be readily applied for a wide class of similar problems, such as spread of epidemics through spatial networks.

Spatial heterogeneities and variability of karst hydro-system : insights from geophysics

Science.gov (United States)

Champollion, C.; Fores, B.; Lesparre, N.; Frederic, N.

2017-12-01

Heterogeneous systems such as karsts or fractured hydro-systems are challenging for both scientist and groundwater resources management. Karsts heterogeneities prevent the comparison and moreover the combination of data representative of different scales: borehole water level can generally not be used directly to interpret spring flow dynamic for example. The spatial heterogeneity has also an impact on the temporal variability of groundwater transfer and storage. Karst hydro-systems have characteristic non linear relation between precipitation amount and discharge at the outlets with threshold effects and a large variability of groundwater transit times In the presentation, geophysical field experiments conducted in karst hydro-system in the south of France are used to investigate groundwater transfer and storage variability at a scale of a few hundred meters. We focus on the added value of both geophysical time-lapse gravity experiments and 2D ERT imaging of the subsurface heterogeneities. Both gravity and ERT results can only be interpreted with large ambiguity or some strong a priori: the relation between resistivity and water content is not unique; almost no information about the processes can be inferred from the groundwater stock variations. The present study demonstrate how the ERT and gravity field experiments can be interpreted together in a coherent scheme with less ambiguity. First the geological and hydro-meteorological context is presented. Then the ERT field experiment including the processing and the results are detailed in the section about geophysical imaging of the heterogeneities. The gravity double difference (S2D) time-lapse experiment is described in the section about geophysical monitoring of the temporal variability. The following discussion demonstrate the impact of both experiments on the interpretation in terms of processes and heterogeneities.

A Study of the Groundwater Level Spatial Variability in the Messara Valley of Crete

Science.gov (United States)

Varouchakis, E. A.; Hristopulos, D. T.; Karatzas, G. P.

2009-04-01

The island of Crete (Greece) has a dry sub-humid climate and marginal groundwater resources, which are extensively used for agricultural activities and human consumption. The Messara valley is located in the south of the Heraklion prefecture, it covers an area of 398 km2, and it is the largest and most productive valley of the island. Over-exploitation during the past thirty (30) years has led to a dramatic decrease of thirty five (35) meters in the groundwater level. Possible future climatic changes in the Mediterranean region, potential desertification, population increase, and extensive agricultural activity generate concern over the sustainability of the water resources of the area. The accurate estimation of the water table depth is important for an integrated groundwater resource management plan. This study focuses on the Mires basin of the Messara valley for reasons of hydro-geological data availability and geological homogeneity. The research goal is to model and map the spatial variability of the basin's groundwater level accurately. The data used in this study consist of seventy (70) piezometric head measurements for the hydrological year 2001-2002. These are unevenly distributed and mostly concentrated along a temporary river that crosses the basin. The range of piezometric heads varies from an extreme low value of 9.4 meters above sea level (masl) to 62 masl, for the wet period of the year (October to April). An initial goal of the study is to develop spatial models for the accurate generation of static maps of groundwater level. At a second stage, these maps should extend the models to dynamic (space-time) situations for the prediction of future water levels. Preliminary data analysis shows that the piezometric head variations are not normally distributed. Several methods including Box-Cox transformation and a modified version of it, transgaussian Kriging, and Gaussian anamorphosis have been used to obtain a spatial model for the piezometric head. A

Internal variability in a regional climate model over West Africa

Energy Technology Data Exchange (ETDEWEB)

Vanvyve, Emilie; Ypersele, Jean-Pascal van [Universite catholique de Louvain, Institut d' astronomie et de geophysique Georges Lemaitre, Louvain-la-Neuve (Belgium); Hall, Nicholas [Laboratoire d' Etudes en Geophysique et Oceanographie Spatiales/Centre National d' Etudes Spatiales, Toulouse Cedex 9 (France); Messager, Christophe [University of Leeds, Institute for Atmospheric Science, Environment, School of Earth and Environment, Leeds (United Kingdom); Leroux, Stephanie [Universite Joseph Fourier, Laboratoire d' etude des Transferts en Hydrologie et Environnement, BP53, Grenoble Cedex 9 (France)

2008-02-15

Sensitivity studies with regional climate models are often performed on the basis of a few simulations for which the difference is analysed and the statistical significance is often taken for granted. In this study we present some simple measures of the confidence limits for these types of experiments by analysing the internal variability of a regional climate model run over West Africa. Two 1-year long simulations, differing only in their initial conditions, are compared. The difference between the two runs gives a measure of the internal variability of the model and an indication of which timescales are reliable for analysis. The results are analysed for a range of timescales and spatial scales, and quantitative measures of the confidence limits for regional model simulations are diagnosed for a selection of study areas for rainfall, low level temperature and wind. As the averaging period or spatial scale is increased, the signal due to internal variability gets smaller and confidence in the simulations increases. This occurs more rapidly for variations in precipitation, which appear essentially random, than for dynamical variables, which show some organisation on larger scales. (orig.)

Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm

Directory of Open Access Journals (Sweden)

Asma Shaheen

2018-03-01

Full Text Available In third world countries, industries mainly cause environmental contamination due to lack of environmental policies or oversight during their implementation. The Sheikhupura industrial zone, which includes industries such as tanneries, leather, chemical, textiles, and colour and dyes, contributes massive amounts of untreated effluents that are released directly into drains and used for the irrigation of crops and vegetables. This practice causes not only soil contamination with an excessive amount of heavy metals, but is also considered a source of toxicity in the food chain, i.e., bioaccumulation in plants and ultimately in human body organs. The objective of this research study was to assess the spatial distribution of the heavy metals chromium (Cr, cadmium (Cd, and lead (Pb, at three depths of soil using geostatistics and the selection of significant contributing variables to soil contamination using the Random Forest (RF function of the Boruta Algorithm. A total of 60 sampling locations were selected in the study area to collect soil samples (180 samples at three depths (0–15 cm, 15–30 cm, and 60–90 cm. The soil samples were analysed for their physico-chemical properties, i.e., soil saturation, electrical conductivity (EC, organic matter (OM, pH, phosphorus (P, potassium (K, and Cr, Cd, and Pb using standard laboratory procedures. The data were analysed with comprehensive statistics and geostatistical techniques. The correlation coefficient matrix between the heavy metals and the physico-chemical properties revealed that electrical conductivity (EC had a significant (p ≤ 0.05 negative correlation with Cr, Cd, and Pb. The RF function of the Boruta Algorithm employed soil depth as a classifier and ranked the significant soil contamination parameters (Cr, Cd, Pb, EC, and P in relation to depth. The mobility factor indicated the leachate percentage of heavy metals at different vertical depths of soil. The spatial distribution pattern of

Small Scale Spatial Variability of Apparent Electrical Conductivity within a Paddy Field

International Nuclear Information System (INIS)

Aimrun, W.; Amin, M.S.M.; Ezrin, M.H.; Amin, M.S.M.

2010-01-01

Quick variability description is an important component for zone management practices. Precision farming requires topping up of only the nutrients that are lacking in the soil to attain the highest yield with the least input. The apparent soil electrical conductivity (ECa) sensor is a useful tool in mapping to identify areas of contrasting soil properties. In non saline soils, ECa is a substitute measurement for soil texture. It is directly related to both water holding capacity and Cation Exchange Capacity (CEC), which are key ingredients of productivity. This sensor measures the ECa across a field quickly and gives detailed soil features (one-second interval) with few operators. Hence, a dense sampling is possible and therefore a high-resolution ECa map can be produced. This study aims to characterize the variability of soil ECa within a Malaysian paddy field with respect to the spatial and seasonal variability. The study was conducted at Block C, Sawah Sempadan, Selangor, Malaysia, for three continuous seasons. Soil ECa was collected after harvesting period. The results showed that deep ECa visualized the pattern of the former river routes clearly as continuous lines (about 45 m width) at the northern and central regions of the study area. This exploration has shown different maps with higher contrast as compared to the existing soil series map for the study area. Seasonal variability test showed that the ECa that was acquired during rainy season (collected after harvest in December to January) has the highest value as compared to another season.

Assimilation of temperature and hydraulic gradients for quantifying the spatial variability of streambed hydraulics

Science.gov (United States)

Huang, Xiang; Andrews, Charles B.; Liu, Jie; Yao, Yingying; Liu, Chuankun; Tyler, Scott W.; Selker, John S.; Zheng, Chunmiao

2016-08-01

Understanding the spatial and temporal characteristics of water flux into or out of shallow aquifers is imperative for water resources management and eco-environmental conservation. In this study, the spatial variability in the vertical specific fluxes and hydraulic conductivities in a streambed were evaluated by integrating distributed temperature sensing (DTS) data and vertical hydraulic gradients into an ensemble Kalman filter (EnKF) and smoother (EnKS) and an empirical thermal-mixing model. The formulation of the EnKF/EnKS assimilation scheme is based on a discretized 1D advection-conduction equation of heat transfer in the streambed. We first systematically tested a synthetic case and performed quantitative and statistical analyses to evaluate the performance of the assimilation schemes. Then a real-world case was evaluated to calculate assimilated specific flux. An initial estimate of the spatial distributions of the vertical hydraulic gradients was obtained from an empirical thermal-mixing model under steady-state conditions using a constant vertical hydraulic conductivity. Then, this initial estimate was updated by repeatedly dividing the assimilated specific flux by estimates of the vertical hydraulic gradients to obtain a refined spatial distribution of vertical hydraulic gradients and vertical hydraulic conductivities. Our results indicate that optimal parameters can be derived with fewer iterations but greater simulation effort using the EnKS compared with the EnKF. For the field application in a stream segment of the Heihe River Basin in northwest China, the average vertical hydraulic conductivities in the streambed varied over three orders of magnitude (5 × 10-1 to 5 × 102 m/d). The specific fluxes ranged from near zero (qz < ±0.05 m/d) to ±1.0 m/d, while the vertical hydraulic gradients were within the range of -0.2 to 0.15 m/m. The highest and most variable fluxes occurred adjacent to a debris-dam and bridge pier. This phenomenon is very likely

Spatial variability of soil carbon stock in the Urucu river basin, Central Amazon-Brazil

International Nuclear Information System (INIS)

Ceddia, Marcos Bacis; Villela, André Luis Oliveira; Pinheiro, Érika Flávia Machado; Wendroth, Ole

2015-01-01

The Amazon Forest plays a major role in C sequestration and release. However, few regional estimates of soil organic carbon (SOC) stock in this ecoregion exist. One of the barriers to improve SOC estimates is the lack of recent soil data at high spatial resolution, which hampers the application of new methods for mapping SOC stock. The aims of this work were: (i) to quantify SOC stock under undisturbed vegetation for the 0–30 and the 0–100 cm under Amazon Forest; (ii) to correlate the SOC stock with soil mapping units and relief attributes and (iii) to evaluate three geostatistical techniques to generate maps of SOC stock (ordinary, isotopic and heterotopic cokriging). The study site is located in the Central region of Amazon State, Brazil. The soil survey covered the study site that has an area of 80 km 2 and resulted in a 1:10,000 soil map. It consisted of 315 field observations (96 complete soil profiles and 219 boreholes). SOC stock was calculated by summing C stocks by horizon, determined as a product of BD, SOC and the horizon thickness. For each one of the 315 soil observations, relief attributes were derived from a topographic map to understand SOC dynamics. The SOC stocks across 30 and 100 cm soil depth were 3.28 and 7.32 kg C m −2 , respectively, which is, 34 and 16%, lower than other studies. The SOC stock is higher in soils developed in relief forms exhibiting well-drained soils, which are covered by Upland Dense Tropical Rainforest. Only SOC stock in the upper 100 cm exhibited spatial dependence allowing the generation of spatial variability maps based on spatial (co)-regionalization. The CTI was inversely correlated with SOC stock and was the only auxiliary variable feasible to be used in cokriging interpolation. The heterotopic cokriging presented the best performance for mapping SOC stock. - Highlights: • The SOC stocks across 30 and 100 cm depth were 3.28 and 7.32 kg C m −2 , respectively. • SOC stocks were 34 and 16%, respectively

Spatial variability of the structure of the lower troposphere over north western Indian Ocean during 1983 summer monsoon

Digital Repository Service at National Institute of Oceanography (India)

RameshKumar, M.R.; Sadhuram, Y.; Michael, G.S.; Rao, L.V.G.

The spatial variability of the structure of the lower troposphere over the north western Indian Ocean during the period 12th July to 2nd September, 1983 has been studied using the upper air data collected during the first scientific cruise of @i...

Temporal and Spatial Variability of Droughts in Southwest China from 1961 to 2012

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Yaohuan Huang

2015-10-01

Full Text Available Southwest China (SC has suffered a series of super extreme droughts in the last decade. This study analyzed the temporal and spatial variations of drought in SC from 1961 to 2012. Based on precipitation anomaly index (PAI that was derived from 1 km gridded precipitation data, three time scales (month, year and decade for the drought frequency (DF and drought area were applied to estimate the spatio-temporal structure of droughts. A time-series analysis showed that winter droughts and spring droughts occurred frequently for almost half of the year from November to March. Summer droughts occasionally occurred in severe drought decades: the 1960s, 1980s and 2000s. During the period of observation, the percent of drought area in SC increased from the 1960s (<5% to the 2000s (>25%. A total of 57% of the area was affected by drought in 2011, when the area experienced its most severe drought both in terms of area and severity. The spatial analysis, which benefitted from the gridded data, detailed that all of SC is at drought risk except for the central Sichuan Basin. The area at high risk for severe and extreme droughts was localized in the mountains of the junction of Sichuan and Yunnan. The temporal and spatial variability can be prerequisites for drought resistance planning and drought risk management of SC.

Selection for altruism through random drift in variable size populations

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Houchmandzadeh Bahram

2012-05-01

Full Text Available Abstract Background Altruistic behavior is defined as helping others at a cost to oneself and a lowered fitness. The lower fitness implies that altruists should be selected against, which is in contradiction with their widespread presence is nature. Present models of selection for altruism (kin or multilevel show that altruistic behaviors can have ‘hidden’ advantages if the ‘common good’ produced by altruists is restricted to some related or unrelated groups. These models are mostly deterministic, or assume a frequency dependent fitness. Results Evolutionary dynamics is a competition between deterministic selection pressure and stochastic events due to random sampling from one generation to the next. We show here that an altruistic allele extending the carrying capacity of the habitat can win by increasing the random drift of “selfish” alleles. In other terms, the fixation probability of altruistic genes can be higher than those of a selfish ones, even though altruists have a smaller fitness. Moreover when populations are geographically structured, the altruists advantage can be highly amplified and the fixation probability of selfish genes can tend toward zero. The above results are obtained both by numerical and analytical calculations. Analytical results are obtained in the limit of large populations. Conclusions The theory we present does not involve kin or multilevel selection, but is based on the existence of random drift in variable size populations. The model is a generalization of the original Fisher-Wright and Moran models where the carrying capacity depends on the number of altruists.

Physical Activity Improves Verbal and Spatial Memory in Older Adults with Probable Mild Cognitive Impairment: A 6-Month Randomized Controlled Trial

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Lindsay S. Nagamatsu

2013-01-01

Full Text Available We report secondary findings from a randomized controlled trial on the effects of exercise on memory in older adults with probable MCI. We randomized 86 women aged 70–80 years with subjective memory complaints into one of three groups: resistance training, aerobic training, or balance and tone (control. All participants exercised twice per week for six months. We measured verbal memory and learning using the Rey Auditory Verbal Learning Test (RAVLT and spatial memory using a computerized test, before and after trial completion. We found that the aerobic training group remembered significantly more items in the loss after interference condition of the RAVLT compared with the control group after six months of training. In addition, both experimental groups showed improved spatial memory performance in the most difficult condition where they were required to memorize the spatial location of three items, compared with the control group. Lastly, we found a significant correlation between spatial memory performance and overall physical capacity after intervention in the aerobic training group. Taken together, our results provide support for the prevailing notion that exercise can positively impact cognitive functioning and may represent an effective strategy to improve memory in those who have begun to experience cognitive decline.

Sea-level rise impacts on the temporal and spatial variability of extreme water levels: A case study for St. Peter-Ording, Germany

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Santamaria-Aguilar, S.; Arns, A.; Vafeidis, A. T.

2017-04-01

Both the temporal and spatial variability of storm surge water level (WL) curves are usually not taken into account in flood risk assessments as observational data are often scarce. In addition, sea-level rise (SLR) can further affect the variability of WLs. We analyze the temporal and spatial variability of the WL curve of 75 historical storm surge events that have been numerically simulated for St. Peter-Ording at the German North Sea coast, considering the effects induced by three SLR scenarios (RCP 4.5, RCP 8.5, and a RCP 8.5 high end scenario). We assess potential impacts of these scenarios on two parameters related to flooding: overflow volumes and fullness. Our results indicate that due to both the temporal and spatial variability of those events the resulting overflow volume can be two or even three times greater. We observe a steepening of the WL curve with an increase of the tidal range under the three SLR scenarios, although SLR induced effects are relatively higher for the RCP 4.5. The steepening of the WL curve with SLR produces a reduction of the fullness, but the changes in overflow volumes also depend on the magnitude of the storm surge event.

Decadal changes of reference crop evapotranspiration attribution: Spatial and temporal variability over China 1960-2011

Science.gov (United States)

Fan, Ze-Xin; Thomas, Axel

2018-05-01

Atmospheric evaporative demand can be used as a measure of the hydrological cycle and the global energy balance. Its long-term variation and the role of driving climatic factors have received increasingly attention in climate change studies. FAO-Penman-Monteith reference crop evapotranspiration rates were estimated for 644 meteorological stations over China for the period 1960-2011 to analyze spatial and temporal attribution variability. Attribution of climatic variables to reference crop evapotranspiration rates was not stable over the study period. While for all of China the contribution of sunshine duration remained relatively stable, the importance of relative humidity increased considerably during the last two decades, particularly in winter. Spatially distributed attribution analysis shows that the position of the center of maximum contribution of sunshine duration has shifted from Southeast to Northeast China while in West China the contribution of wind speed has decreased dramatically. In contrast relative humidity has become an important factor in most parts of China. Changes in the Asian Monsoon circulation may be responsible for altered patterns of cloudiness and a general decrease of wind speeds over China. The continuously low importance of temperature confirms that global warming does not necessarily lead to rising atmospheric evaporative demand.

The Significance of the Spatial Variability of Rainfall on the Numerical Simulation of Urban Floods

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Laurent Guillaume Courty

2018-02-01

Full Text Available The growth of urban population, combined with an increase of extreme events due to climate change call for a better understanding and representation of urban floods. The uncertainty in rainfall distribution is one of the most important factors that affects the watershed response to a given precipitation event. However, most of the investigations on this topic have considered theoretical scenarios, with little reference to case studies in the real world. This paper incorporates the use of spatially-variable precipitation data from a long-range radar in the simulation of the severe floods that impacted the city of Hull, U.K., in June 2007. This radar-based rainfall field is merged with rain gauge data using a Kriging with External Drift interpolation technique. The utility of this spatially-variable information is investigated through the comparison of computed flooded areas (uniform and radar against those registered by public authorities. Both results show similar skills at reproducing the real event, but differences in the total precipitated volumes, water depths and flooded areas are illustrated. It is envisaged that in urban areas and with the advent of higher resolution radars, these differences will be more important and call for further investigation.

Spatial patterns of North Atlantic Oscillation influence on mass balance variability of European glaciers

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B. Marzeion

2012-06-01

Full Text Available We present and validate a set of minimal models of glacier mass balance variability. The most skillful model is then applied to reconstruct 7735 individual time series of mass balance variability for all glaciers in the European Alps and Scandinavia. Subsequently, we investigate the influence of atmospheric variability associated with the North Atlantic Oscillation (NAO on the glaciers' mass balances.

We find a spatial coherence in the glaciers' sensitivity to NAO forcing which is caused by regionally similar mechanisms relating the NAO forcing to the mass balance: in southwestern Scandinavia, winter precipitation causes a correlation of mass balances with the NAO. In northern Scandinavia, temperature anomalies outside the core winter season cause an anti-correlation between NAO and mass balances. In the western Alps, both temperature and winter precipitation anomalies lead to a weak anti-correlation of mass balances with the NAO, while in the eastern Alps, the influences of winter precipitation and temperature anomalies tend to cancel each other, and only on the southern side a slight anti-correlation of mass balances with the NAO prevails.

A Survey of Spatial and Seasonal Water Isotope Variability on the Juneau Icefield, Alaksa

Science.gov (United States)

Dennis, D.; Carter, A.; Clinger, A. E.; Eads, O. L.; Gotwals, S.; Gunderson, J.; Hollyday, A. E.; Klein, E. S.; Markle, B. R.; Timms, J. R.

2015-12-01

The depletion of stable oxygen-hydrogen isotopes (δ18O and δH) is well correlated with temperature change, which is driven by variation in topography, climate, and atmospheric circulation. This study presents a survey of the spatial and seasonal variability of isotopic signatures on the Juneau Icefield (JI), Alaska, USA which spans over 3,000 square-kilometers. To examine small scale variability in the previous year's accumulation, samples were taken at regular intervals from snow pits and a one square-kilometer surficial grid. Surface snow samples were collected across the icefield to evaluate large scale variability, ranging approximately 1,000 meters in elevation and 100 kilometers in distance. Individual precipitation events were also sampled to track percolation throughout the snowpack and temperature correlations. A survey of this extent has never been undertaken on the JI. Samples were analyzed in the field using a Los Gatos laser isotope analyzer. This survey helps us better understand isotope fractionation on temperate glaciers in coastal environments and provides preliminary information on the suitability of the JI for a future ice core drilling project.

Estimation of the temperature spatial variability in confined spaces based on thermal imaging

Science.gov (United States)

Augustyn, Grzegorz; Jurasz, Jakub; Jurczyk, Krzysztof; Korbiel, Tomasz; Mikulik, Jerzy; Pawlik, Marcin; Rumin, Rafał

2017-11-01

In developed countries the salaries of office workers are several times higher than the total cost of maintaining and operating the building. Therefore even a small improvement in human work productivity and performance as a result of enhancing the quality of their work environment may lead to a meaningful economic benefits. The air temperature is the most commonly used indicator in assessing the indoor environment quality. What is more, it is well known that thermal comfort has the biggest impact on employees performance and their ability to work efficiently. In majority of office buildings, indoor temperature is managed by heating, ventilation and air conditioning (HVAC) appliances. However the way how they are currently managed and controlled leads to the nonhomogeneous distribution of temperature in certain space. An approach to determining the spatial variability of temperature in confined spaces was introduced based on thermal imaging temperature measurements. The conducted research and obtained results enabled positive verification of the method and creation of surface plot illustrating the temperature variability.

Spatial Downscaling of Alien Species Presences using Machine Learning

Science.gov (United States)

Daliakopoulos, Ioannis N.; Katsanevakis, Stelios; Moustakas, Aristides

2017-07-01

Large scale, high-resolution data on alien species distributions are essential for spatially explicit assessments of their environmental and socio-economic impacts, and management interventions for mitigation. However, these data are often unavailable. This paper presents a method that relies on Random Forest (RF) models to distribute alien species presence counts at a finer resolution grid, thus achieving spatial downscaling. A sufficiently large number of RF models are trained using random subsets of the dataset as predictors, in a bootstrapping approach to account for the uncertainty introduced by the subset selection. The method is tested with an approximately 8×8 km2 grid containing floral alien species presence and several indices of climatic, habitat, land use covariates for the Mediterranean island of Crete, Greece. Alien species presence is aggregated at 16×16 km2 and used as a predictor of presence at the original resolution, thus simulating spatial downscaling. Potential explanatory variables included habitat types, land cover richness, endemic species richness, soil type, temperature, precipitation, and freshwater availability. Uncertainty assessment of the spatial downscaling of alien species’ occurrences was also performed and true/false presences and absences were quantified. The approach is promising for downscaling alien species datasets of larger spatial scale but coarse resolution, where the underlying environmental information is available at a finer resolution than the alien species data. Furthermore, the RF architecture allows for tuning towards operationally optimal sensitivity and specificity, thus providing a decision support tool for designing a resource efficient alien species census.

Concordance among different aquatic insect assemblages and the relative role of spatial and environmental variables

OpenAIRE

Chunyan Qin; Yong Zhang; Haiyan Yu; Beixin Wang

2013-01-01

Indicator groups are often used for biodiversity monitoring and conservation, however, the effectiveness of these groups in representing biodiversity is rarely tested. To explore community congruence among different aquatic insect groups and how this may be affected by spatial factors and environmental variables, we carried out an investigation on aquatic insects in April 2010 in 21 headwater streams within the Dongtiaoxi Basin, China. In total, we recorded 130 species from 92 genera, 44 fami...

A method for estimating spatially variable seepage and hydrualic conductivity in channels with very mild slopes

Science.gov (United States)

Shanafield, Margaret; Niswonger, Richard G.; Prudic, David E.; Pohll, Greg; Susfalk, Richard; Panday, Sorab

2014-01-01

Infiltration along ephemeral channels plays an important role in groundwater recharge in arid regions. A model is presented for estimating spatial variability of seepage due to streambed heterogeneity along channels based on measurements of streamflow-front velocities in initially dry channels. The diffusion-wave approximation to the Saint-Venant equations, coupled with Philip's equation for infiltration, is connected to the groundwater model MODFLOW and is calibrated by adjusting the saturated hydraulic conductivity of the channel bed. The model is applied to portions of two large water delivery canals, which serve as proxies for natural ephemeral streams. Estimated seepage rates compare well with previously published values. Possible sources of error stem from uncertainty in Manning's roughness coefficients, soil hydraulic properties and channel geometry. Model performance would be most improved through more frequent longitudinal estimates of channel geometry and thalweg elevation, and with measurements of stream stage over time to constrain wave timing and shape. This model is a potentially valuable tool for estimating spatial variability in longitudinal seepage along intermittent and ephemeral channels over a wide range of bed slopes and the influence of seepage rates on groundwater levels.

Uncertainties in repository performance from spatial variability of hydraulic conductivities - statistical estimation and stochastic simulation using PROPER

International Nuclear Information System (INIS)

Lovius, L.; Norman, S.; Kjellbert, N.

1990-02-01

An assessment has been made of the impact of spatial variability on the performance of a KBS-3 type repository. The uncertainties in geohydrologically related performance measures have been investigated using conductivity data from one of the Swedish study sites. The analysis was carried out with the PROPER code and the FSCF10 submodel. (authors)

The Effect of Restoration Treatments on the Spatial Variability of Soil Processes under Longleaf Pine Trees

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John K. Hiers

2012-08-01

Full Text Available The objectives of this study were to (1 characterize tree-based spatial patterning of soil properties and understory vegetation in frequently burned (“reference state” and fire-suppressed longleaf pine forests; and (2 determine how restoration treatments affected patterning. To attain these objectives, we used an experimental manipulation of management types implemented 15 years ago in Florida. We randomly located six mature longleaf pine trees in one reference and four restoration treatments (i.e., burn, control, herbicide, and mechanical, for a total of 36 trees. In addition to the original treatments and as part of a monitoring program, all plots were subjected to several prescribed fires during these 15 years. Under each tree, we sampled mineral soil and understory vegetation at 1 m, 2 m, 3 m and 4 m (vegetation only away from the tree. At these sites, soil carbon and nitrogen were higher near the trunk while graminoids, forbs and saw palmetto covers showed an opposite trend. Our results confirmed that longleaf pine trees affect the spatial patterning of soil and understory vegetation, and this patterning was mostly limited to the restoration sites. We suggest frequent burning as a probable cause for a lack of spatial structure in the “reference state”. We attribute the presence of spatial patterning in the restoration sites to accumulation of organic materials near the base of mature trees.

GIS-modelling of the spatial variability of flash flood hazard in Abu Dabbab catchment, Red Sea Region, Egypt

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Islam Abou El-Magd

2010-06-01

Full Text Available In the mountainous area of the Red Sea region in southeastern Egypt, the development of new mining activities or/and domestic infrastructures require reliable and accurate information about natural hazards particularly flash flood. This paper presents the assessment of flash flood hazards in the Abu Dabbab drainage basin. Remotely sensed data were used to delineate the alluvial active channels, which were integrated with morphometric parameters extracted from digital elevation models (DEM into geographical information systems (GIS to construct a hydrological model that provides estimates about the amount of surface runoff as well as the magnitude of flash floods. The peak discharge is randomly varied at different cross-sections along the main channel. Under consistent 10 mm rainfall event, the selected cross-section in middle of the main channel is prone to maximum water depth at 80 cm, which decreases to nearly 30 cm at the outlet due to transmission loss. The estimation of spatial variability of flow parameters within the catchment at different confluences of the constituting sub-catchments can be considered and used in planning for engineering foundations and linear infrastructures with the least flash flood hazard. Such information would, indeed, help decision makers and planning to minimize such hazards.

Discrete random walk models for space-time fractional diffusion

International Nuclear Information System (INIS)

Gorenflo, Rudolf; Mainardi, Francesco; Moretti, Daniele; Pagnini, Gianni; Paradisi, Paolo

2002-01-01

A physical-mathematical approach to anomalous diffusion may be based on generalized diffusion equations (containing derivatives of fractional order in space or/and time) and related random walk models. By space-time fractional diffusion equation we mean an evolution equation obtained from the standard linear diffusion equation by replacing the second-order space derivative with a Riesz-Feller derivative of order α is part of (0,2] and skewness θ (moduleθ≤{α,2-α}), and the first-order time derivative with a Caputo derivative of order β is part of (0,1]. Such evolution equation implies for the flux a fractional Fick's law which accounts for spatial and temporal non-locality. The fundamental solution (for the Cauchy problem) of the fractional diffusion equation can be interpreted as a probability density evolving in time of a peculiar self-similar stochastic process that we view as a generalized diffusion process. By adopting appropriate finite-difference schemes of solution, we generate models of random walk discrete in space and time suitable for simulating random variables whose spatial probability density evolves in time according to this fractional diffusion equation

Soil salinity and acidity : spatial variabil[it]y and effects on rice production in West Africa's mangrove zone

NARCIS (Netherlands)

Sylla, M.

1994-01-01

In the mangrove environment of West Africa, high spatial and temporal variability of soil constraints (salinity and acidity) to rice production is a problem for the transfer and adoption of new agronomic techniques, for land use planning, and for soil and water management. Recently, several

Physical activity, mindfulness meditation, or heart rate variability biofeedback for stress reduction: a randomized controlled trial

NARCIS (Netherlands)

van der Zwan, J.E.; de Vente, W.; Huizink, A.C.; Bögels, S.M.; de Bruin, E.I.

2015-01-01

In contemporary western societies stress is highly prevalent, therefore the need for stress-reducing methods is great. This randomized controlled trial compared the efficacy of self-help physical activity (PA), mindfulness meditation (MM), and heart rate variability biofeedback (HRV-BF) in reducing

An MGF-based unified framework to determine the joint statistics of partial sums of ordered random variables

KAUST Repository

Nam, Sungsik; Alouini, Mohamed-Slim; Yang, Hongchuan

2010-01-01

Order statistics find applications in various areas of communications and signal processing. In this paper, we introduce an unified analytical framework to determine the joint statistics of partial sums of ordered random variables (RVs

Fine-scale spatial variability of heat-related mortality in Philadelphia County, USA, from 1983-2008: a case-series analysis

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Hondula David M

2012-03-01

Full Text Available Abstract Background High temperature and humidity conditions are associated with short-term elevations in the mortality rate in many United States cities. Previous research has quantified this relationship in an aggregate manner over large metropolitan areas, but within these areas the response may differ based on local-scale variability in climate, population characteristics, and socio-economic factors. Methods We compared the mortality response for 48 Zip Code Tabulation Areas (ZCTAs comprising Philadelphia County, PA to determine if certain areas are associated with elevated risk during high heat stress conditions. A randomization test was used to identify mortality exceedances for various apparent temperature thresholds at both the city and local scale. We then sought to identify the environmental, demographic, and social factors associated with high-risk areas via principal components regression. Results Citywide mortality increases by 9.3% on days following those with apparent temperatures over 34°C observed at 7:00 p.m. local time. During these conditions, elevated mortality rates were found for 10 of the 48 ZCTAs concentrated in the west-central portion of the County. Factors related to high heat mortality risk included proximity to locally high surface temperatures, low socioeconomic status, high density residential zoning, and age. Conclusions Within the larger Philadelphia metropolitan area, there exists statistically significant fine-scale spatial variability in the mortality response to high apparent temperatures. Future heat warning systems and mitigation and intervention measures could target these high risk areas to reduce the burden of extreme weather on summertime morbidity and mortality.

Variability in results from negative binomial models for Lyme disease measured at different spatial scales.

Science.gov (United States)

Tran, Phoebe; Waller, Lance

2015-01-01

Lyme disease has been the subject of many studies due to increasing incidence rates year after year and the severe complications that can arise in later stages of the disease. Negative binomial models have been used to model Lyme disease in the past with some success. However, there has been little focus on the reliability and consistency of these models when they are used to study Lyme disease at multiple spatial scales. This study seeks to explore how sensitive/consistent negative binomial models are when they are used to study Lyme disease at different spatial scales (at the regional and sub-regional levels). The study area includes the thirteen states in the Northeastern United States with the highest Lyme disease incidence during the 2002-2006 period. Lyme disease incidence at county level for the period of 2002-2006 was linked with several previously identified key landscape and climatic variables in a negative binomial regression model for the Northeastern region and two smaller sub-regions (the New England sub-region and the Mid-Atlantic sub-region). This study found that negative binomial models, indeed, were sensitive/inconsistent when used at different spatial scales. We discuss various plausible explanations for such behavior of negative binomial models. Further investigation of the inconsistency and sensitivity of negative binomial models when used at different spatial scales is important for not only future Lyme disease studies and Lyme disease risk assessment/management but any study that requires use of this model type in a spatial context. Copyright © 2014 Elsevier Inc. All rights reserved.

Spatial variability of noise level in agricultural machines Variabilidade espacial do nível de ruído em máquinas agrícolas

OpenAIRE

Tadayuki Yanagi Junior; Leonardo Schiassi; Diogo F. Rossoni; Patrícia F. Ponciano; Renato R. de Lima

2012-01-01

The knowledge of the spatial variability of noise levels and the build of kriging maps can help the evaluation of the salubrity of environments occupied by agricultural workers. Therefore, the objective of this research was to characterize the spatial variability of the noise level generated by four agricultural machines, using geostatistics, and to verify if the values are within the limits of human comfort. The evaluated machines were: harvester, chainsaw, brushcutter and tractor. The data ...

Violation of Bell's inequality with continuous spatial variables

International Nuclear Information System (INIS)

Abouraddy, Ayman F.; Yarnall, Timothy; Saleh, Bahaa E. A.; Teich, Malvin C.

2007-01-01

The Einstein-Podolsky-Rosen (EPR) argument revealed the paradoxical properties of a two-particle system entangled continuously in the spatial parameter. Yet a direct test of quantum nonlocality exhibited by this state, via a violation of Bell's inequality, has not been forthcoming. In this paper, we identify and construct experimental arrangements comprising simple optical components, without nonlinearities or moving parts, that implement operators in the spatial-parity space of single-photon fields that correspond to the familiar Pauli spin operators. We achieve this by first establishing an isomorphism between the single-mode multiphoton electromagnetic-field space spanned by a Fock-state basis and the single-photon multimode electromagnetic-field space spanned by a spatial-eigenmode basis. We then proceed to construct a Hilbert space with a two-dimensional basis of spatial even-odd parity modes. In particular, we describe an arrangement that implements a rotation in the parity space of each photon of an entangled-photon pair, allowing for a straightforward experimental test of Bell's inequality using the EPR state. Finally, the violation of a Bell inequality is quantified in terms of the physical parameters of the two-photon source

Atmospheric mechanisms governing the spatial and temporal variability of phenological phases in central Europe

Science.gov (United States)

Scheifinger, Helfried; Menzel, Annette; Koch, Elisabeth; Peter, Christian; Ahas, Rein

2002-11-01

A data set of 17 phenological phases from Germany, Austria, Switzerland and Slovenia spanning the time period from 1951 to 1998 has been made available for analysis together with a gridded temperature data set (1° × 1° grid) and the North Atlantic Oscillation (NAO) index time series. The disturbances of the westerlies constitute the main atmospheric source for the temporal variability of phenological events in Europe. The trend, the standard deviation and the discontinuity of the phenological time series at the end of the 1980s can, to a great extent, be explained by the NAO. A number of factors modulate the influence of the NAO in time and space. The seasonal northward shift of the westerlies overlaps with the sequence of phenological spring phases, thereby gradually reducing its influence on the temporal variability of phenological events with progression of spring (temporal loss of influence). This temporal process is reflected by a pronounced decrease in trend and standard deviation values and common variability with the NAO with increasing year-day. The reduced influence of the NAO with increasing distance from the Atlantic coast is not only apparent in studies based on the data set of the International Phenological Gardens, but also in the data set of this study with a smaller spatial extent (large-scale loss of influence). The common variance between phenological and NAO time series displays a discontinuous drop from the European Atlantic coast towards the Alps. On a local and regional scale, mountainous terrain reduces the influence of the large-scale atmospheric flow from the Atlantic via a proposed decoupling mechanism. Valleys in mountainous terrain have the inclination to harbour temperature inversions over extended periods of time during the cold season, which isolate the valley climate from the large-scale atmospheric flow at higher altitudes. Most phenological stations reside at valley bottoms and are thus largely decoupled in their temporal

Short-Term Effects of Climatic Variables on Hand, Foot, and Mouth Disease in Mainland China, 2008-2013: A Multilevel Spatial Poisson Regression Model Accounting for Overdispersion.

Science.gov (United States)

Liao, Jiaqiang; Yu, Shicheng; Yang, Fang; Yang, Min; Hu, Yuehua; Zhang, Juying

2016-01-01

Hand, Foot, and Mouth Disease (HFMD) is a worldwide infectious disease. In China, many provinces have reported HFMD cases, especially the south and southwest provinces. Many studies have found a strong association between the incidence of HFMD and climatic factors such as temperature, rainfall, and relative humidity. However, few studies have analyzed cluster effects between various geographical units. The nonlinear relationships and lag effects between weekly HFMD cases and climatic variables were estimated for the period of 2008-2013 using a polynomial distributed lag model. The extra-Poisson multilevel spatial polynomial model was used to model the exact relationship between weekly HFMD incidence and climatic variables after considering cluster effects, provincial correlated structure of HFMD incidence and overdispersion. The smoothing spline methods were used to detect threshold effects between climatic factors and HFMD incidence. The HFMD incidence spatial heterogeneity distributed among provinces, and the scale measurement of overdispersion was 548.077. After controlling for long-term trends, spatial heterogeneity and overdispersion, temperature was highly associated with HFMD incidence. Weekly average temperature and weekly temperature difference approximate inverse "V" shape and "V" shape relationships associated with HFMD incidence. The lag effects for weekly average temperature and weekly temperature difference were 3 weeks and 2 weeks. High spatial correlated HFMD incidence were detected in northern, central and southern province. Temperature can be used to explain most of variation of HFMD incidence in southern and northeastern provinces. After adjustment for temperature, eastern and Northern provinces still had high variation HFMD incidence. We found a relatively strong association between weekly HFMD incidence and weekly average temperature. The association between the HFMD incidence and climatic variables spatial heterogeneity distributed across

Mineralogy of the clay fraction of Alfisols in two slope curvatures: III - spatial variability

Directory of Open Access Journals (Sweden)

Livia Arantes Camargo

2013-04-01

Full Text Available A good knowledge of the spatial distribution of clay minerals in the landscape facilitates the understanding of the influence of relief on the content and crystallographic attributes of soil minerals such as goethite, hematite, kaolinite and gibbsite. This study aimed at describing the relationships between the mineral properties of the clay fraction and landscape shapes by determining the mineral properties of goethite, hematite, kaolinite and gibbsite, and assessing their dependence and spatial variability, in two slope curvatures. To this end, two 100 × 100 m grids were used to establish a total of 121 regularly spaced georeferenced sampling nodes 10 m apart. Samples were collected from the layer 0.0-0.2 m and analysed for iron oxides, and kaolinite and gibbsite in the clay fraction. Minerals in the clay fraction were characterized from their X-ray diffraction (XRD patterns, which were interpreted and used to calculate the width at half height (WHH and mean crystallite dimension (MCD of iron oxides, kaolinite, and gibbsite, as well as aluminium substitution and specific surface area (SSA in hematite and goethite. Additional calculations included the goethite and hematite contents, and the goethite/(goethite+hematite [Gt/(Gt+Hm] and kaolinite/(kaolinite+gibbsite [Kt/(Kt+Gb] ratios. Mineral properties were established by statistical analysis of the XRD data, and spatial dependence was assessed geostatistically. Mineralogical properties differed significantly between the convex area and concave area. The geostatistical analysis showed a greater number of mineralogical properties with spatial dependence and a higher range in the convex than in the concave area.

SPATIAL VARIABILITY IN THE MUDPRAWN UPOGEBIA AFRICANA ...

African Journals Online (AJOL)

A nested sampling design was used to examine the variability in density, biomass, sex ratio and size of the estuarine mudprawn Upogebia africana in six estuaries on the south-east coast of South Africa. The objectives were to test the general hypothesis that there is variability in these variables at the scales of regions, ...

Distribution of peak expiratory flow variability by age, gender and smoking habits in a random population sample aged 20-70 yrs

NARCIS (Netherlands)

Boezen, H M; Schouten, J. P.; Postma, D S; Rijcken, B

1994-01-01

Peak expiratory flow (PEF) variability can be considered as an index of bronchial lability. Population studies on PEF variability are few. The purpose of the current paper is to describe the distribution of PEF variability in a random population sample of adults with a wide age range (20-70 yrs),

Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest

Energy Technology Data Exchange (ETDEWEB)

Liu, Jiamin; Hoffman, Joanne; Zhao, Jocelyn; Yao, Jianhua; Lu, Le; Kim, Lauren; Turkbey, Evrim B.; Summers, Ronald M., E-mail: rms@nih.gov [Imaging Biomarkers and Computer-aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center Building, 10 Room 1C224 MSC 1182, Bethesda, Maryland 20892-1182 (United States)

2016-07-15

Purpose: To develop an automated system for mediastinal lymph node detection and station mapping for chest CT. Methods: The contextual organs, trachea, lungs, and spine are first automatically identified to locate the region of interest (ROI) (mediastinum). The authors employ shape features derived from Hessian analysis, local object scale, and circular transformation that are computed per voxel in the ROI. Eight more anatomical structures are simultaneously segmented by multiatlas label fusion. Spatial priors are defined as the relative multidimensional distance vectors corresponding to each structure. Intensity, shape, and spatial prior features are integrated and parsed by a random forest classifier for lymph node detection. The detected candidates are then segmented by the following curve evolution process. Texture features are computed on the segmented lymph nodes and a support vector machine committee is used for final classification. For lymph node station labeling, based on the segmentation results of the above anatomical structures, the textual definitions of mediastinal lymph node map according to the International Association for the Study of Lung Cancer are converted into patient-specific color-coded CT image, where the lymph node station can be automatically assigned for each detected node. Results: The chest CT volumes from 70 patients with 316 enlarged mediastinal lymph nodes are used for validation. For lymph node detection, their system achieves 88% sensitivity at eight false positives per patient. For lymph node station labeling, 84.5% of lymph nodes are correctly assigned to their stations. Conclusions: Multiple-channel shape, intensity, and spatial prior features aggregated by a random forest classifier improve mediastinal lymph node detection on chest CT. Using the location information of segmented anatomic structures from the multiatlas formulation enables accurate identification of lymph node stations.

Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest

International Nuclear Information System (INIS)

Liu, Jiamin; Hoffman, Joanne; Zhao, Jocelyn; Yao, Jianhua; Lu, Le; Kim, Lauren; Turkbey, Evrim B.; Summers, Ronald M.

2016-01-01

Purpose: To develop an automated system for mediastinal lymph node detection and station mapping for chest CT. Methods: The contextual organs, trachea, lungs, and spine are first automatically identified to locate the region of interest (ROI) (mediastinum). The authors employ shape features derived from Hessian analysis, local object scale, and circular transformation that are computed per voxel in the ROI. Eight more anatomical structures are simultaneously segmented by multiatlas label fusion. Spatial priors are defined as the relative multidimensional distance vectors corresponding to each structure. Intensity, shape, and spatial prior features are integrated and parsed by a random forest classifier for lymph node detection. The detected candidates are then segmented by the following curve evolution process. Texture features are computed on the segmented lymph nodes and a support vector machine committee is used for final classification. For lymph node station labeling, based on the segmentation results of the above anatomical structures, the textual definitions of mediastinal lymph node map according to the International Association for the Study of Lung Cancer are converted into patient-specific color-coded CT image, where the lymph node station can be automatically assigned for each detected node. Results: The chest CT volumes from 70 patients with 316 enlarged mediastinal lymph nodes are used for validation. For lymph node detection, their system achieves 88% sensitivity at eight false positives per patient. For lymph node station labeling, 84.5% of lymph nodes are correctly assigned to their stations. Conclusions: Multiple-channel shape, intensity, and spatial prior features aggregated by a random forest classifier improve mediastinal lymph node detection on chest CT. Using the location information of segmented anatomic structures from the multiatlas formulation enables accurate identification of lymph node stations.

An MGF-based unified framework to determine the joint statistics of partial sums of ordered i.n.d. random variables

KAUST Repository

Nam, Sungsik

2014-08-01

The joint statistics of partial sums of ordered random variables (RVs) are often needed for the accurate performance characterization of a wide variety of wireless communication systems. A unified analytical framework to determine the joint statistics of partial sums of ordered independent and identically distributed (i.i.d.) random variables was recently presented. However, the identical distribution assumption may not be valid in several real-world applications. With this motivation in mind, we consider in this paper the more general case in which the random variables are independent but not necessarily identically distributed (i.n.d.). More specifically, we extend the previous analysis and introduce a new more general unified analytical framework to determine the joint statistics of partial sums of ordered i.n.d. RVs. Our mathematical formalism is illustrated with an application on the exact performance analysis of the capture probability of generalized selection combining (GSC)-based RAKE receivers operating over frequency-selective fading channels with a non-uniform power delay profile. © 1991-2012 IEEE.

Spatial Variability of CCN Sized Aerosol Particles

Science.gov (United States)

Asmi, A.; Väänänen, R.

2014-12-01

The computational limitations restrict the grid size used in GCM models, and for many cloud types they are too large when compared to the scale of the cloud formation processes. Several parameterizations for e.g. convective cloud formation exist, but information on spatial subgrid variation of the cloud condensation nuclei (CCNs) sized aerosol concentration is not known. We quantify this variation as a function of the spatial scale by using datasets from airborne aerosol measurement campaigns around the world including EUCAARI LONGREX, ATAR, INCA, INDOEX, CLAIRE, PEGASOS and several regional airborne campaigns in Finland. The typical shapes of the distributions are analyzed. When possible, we use information obtained by CCN counters. In some other cases, we use particle size distribution measured by for example SMPS to get approximated CCN concentration. Other instruments used include optical particle counters or condensational particle counters. When using the GCM models, the CCN concentration used for each the grid-box is often considered to be either flat, or as an arithmetic mean of the concentration inside the grid-box. However, the aircraft data shows that the concentration values are often lognormal distributed. This, combined with the subgrid variations in the land use and atmospheric properties, might cause that the aerosol-cloud interactions calculated by using mean values to vary significantly from the true effects both temporary and spatially. This, in turn, can cause non-linear bias into the GCMs. We calculate the CCN aerosol concentration distribution as a function of different spatial scales. The measurements allow us to study the variation of these distributions within from hundreds of meters up to hundreds of kilometers. This is used to quantify the potential error when mean values are used in GCMs.

Spatial variability of soil carbon stock in the Urucu river basin, Central Amazon-Brazil

Energy Technology Data Exchange (ETDEWEB)

Ceddia, Marcos Bacis, E-mail: marcosceddia@gmail.com [Department of Soil, Institute of Agronomy, Universidade Federal Rural do Rio de Janeiro (UFRRJ), Seropédica, RJ 23890-000 (Brazil); Villela, André Luis Oliveira [Colégio Técnico da UFRRJ, RJ, Seropédica 23890-000 (Brazil); Pinheiro, Érika Flávia Machado [Department of Soil, Institute of Agronomy, Universidade Federal Rural do Rio de Janeiro (UFRRJ), Seropédica, RJ 23890-000 (Brazil); Wendroth, Ole [Department of Plant & Soil Sciences, University of Kentucky, College of Agriculture, Lexington, KY (United States)

2015-09-01

The Amazon Forest plays a major role in C sequestration and release. However, few regional estimates of soil organic carbon (SOC) stock in this ecoregion exist. One of the barriers to improve SOC estimates is the lack of recent soil data at high spatial resolution, which hampers the application of new methods for mapping SOC stock. The aims of this work were: (i) to quantify SOC stock under undisturbed vegetation for the 0–30 and the 0–100 cm under Amazon Forest; (ii) to correlate the SOC stock with soil mapping units and relief attributes and (iii) to evaluate three geostatistical techniques to generate maps of SOC stock (ordinary, isotopic and heterotopic cokriging). The study site is located in the Central region of Amazon State, Brazil. The soil survey covered the study site that has an area of 80 km{sup 2} and resulted in a 1:10,000 soil map. It consisted of 315 field observations (96 complete soil profiles and 219 boreholes). SOC stock was calculated by summing C stocks by horizon, determined as a product of BD, SOC and the horizon thickness. For each one of the 315 soil observations, relief attributes were derived from a topographic map to understand SOC dynamics. The SOC stocks across 30 and 100 cm soil depth were 3.28 and 7.32 kg C m{sup −2}, respectively, which is, 34 and 16%, lower than other studies. The SOC stock is higher in soils developed in relief forms exhibiting well-drained soils, which are covered by Upland Dense Tropical Rainforest. Only SOC stock in the upper 100 cm exhibited spatial dependence allowing the generation of spatial variability maps based on spatial (co)-regionalization. The CTI was inversely correlated with SOC stock and was the only auxiliary variable feasible to be used in cokriging interpolation. The heterotopic cokriging presented the best performance for mapping SOC stock. - Highlights: • The SOC stocks across 30 and 100 cm depth were 3.28 and 7.32 kg C m{sup −2}, respectively. • SOC stocks were 34 and 16

WHEN THE DISTURBANCES ARE SPATIALLY CORRELATED

African Journals Online (AJOL)

correlation, spatial error process. INTRODUCTION. Consider the linear regression model for spatial correlation y=XB +u, u=Ce, (1) where y is a Txl observable random vector, X is a Txk matrix of known constants with full column rank k, B is a k xl vector of unknown parameters,. :2 is a Txl random vector with expectation zero ...

Estimation of the temperature spatial variability in confined spaces based on thermal imaging

Directory of Open Access Journals (Sweden)

Augustyn Grzegorz

2017-01-01

Full Text Available In developed countries the salaries of office workers are several times higher than the total cost of maintaining and operating the building. Therefore even a small improvement in human work productivity and performance as a result of enhancing the quality of their work environment may lead to a meaningful economic benefits. The air temperature is the most commonly used indicator in assessing the indoor environment quality. What is more, it is well known that thermal comfort has the biggest impact on employees performance and their ability to work efficiently. In majority of office buildings, indoor temperature is managed by heating, ventilation and air conditioning (HVAC appliances. However the way how they are currently managed and controlled leads to the nonhomogeneous distribution of temperature in certain space. An approach to determining the spatial variability of temperature in confined spaces was introduced based on thermal imaging temperature measurements. The conducted research and obtained results enabled positive verification of the method and creation of surface plot illustrating the temperature variability.

Residual and Past Entropy for Concomitants of Ordered Random Variables of Morgenstern Family

Directory of Open Access Journals (Sweden)

M. M. Mohie EL-Din

2015-01-01

Full Text Available For a system, which is observed at time t, the residual and past entropies measure the uncertainty about the remaining and the past life of the distribution, respectively. In this paper, we have presented the residual and past entropy of Morgenstern family based on the concomitants of the different types of generalized order statistics (gos and give the linear transformation of such model. Characterization results for these dynamic entropies for concomitants of ordered random variables have been considered.

A new heat flux model for the Antarctic Peninsula incorporating spatially variable upper crustal radiogenic heat production

Science.gov (United States)

Burton-Johnson, A.; Halpin, J.; Whittaker, J. M.; Graham, F. S.; Watson, S. J.

2017-12-01

We present recently published findings (Burton-Johnson et al., 2017) on the variability of Antarctic sub-glacial heat flux and the impact from upper crustal geology. Our new method reveals that the upper crust contributes up to 70% of the Antarctic Peninsula's subglacial heat flux, and that heat flux values are more variable at smaller spatial resolutions than geophysical methods can resolve. Results indicate a higher heat flux on the east and south of the Peninsula (mean 81 mWm-2) where silicic rocks predominate, than on the west and north (mean 67 mWm-2) where volcanic arc and quartzose sediments are dominant. Whilst the data supports the contribution of HPE-enriched granitic rocks to high heat flux values, sedimentary rocks can be of comparative importance dependent on their provenance and petrography. Models of subglacial heat flux must utilize a heterogeneous upper crust with variable radioactive heat production if they are to accurately predict basal conditions of the ice sheet. Our new methodology and dataset facilitate improved numerical model simulations of ice sheet dynamics. The most significant challenge faced remains accurate determination of crustal structure, particularly the depths of the HPE-enriched sedimentary basins and the sub-glacial geology away from exposed outcrops. Continuing research (particularly detailed geophysical interpretation) will better constrain these unknowns and the effect of upper crustal geology on the Antarctic ice sheet. Burton-Johnson, A., Halpin, J.A., Whittaker, J.M., Graham, F.S., and Watson, S.J., 2017, A new heat flux model for the Antarctic Peninsula incorporating spatially variable upper crustal radiogenic heat production: Geophysical Research Letters, v. 44, doi: 10.1002/2017GL073596.

A method to combine non-probability sample data with probability sample data in estimating spatial means of environmental variables

NARCIS (Netherlands)

Brus, D.J.; Gruijter, de J.J.

2003-01-01

In estimating spatial means of environmental variables of a region from data collected by convenience or purposive sampling, validity of the results can be ensured by collecting additional data through probability sampling. The precision of the pi estimator that uses the probability sample can be

Analysis of the spatial variability of crop yield and soil properties in small agricultural plots

Directory of Open Access Journals (Sweden)

Vieira Sidney Rosa

2003-01-01

Full Text Available The objective of this study was to assess spatial variability of soil properties and crop yield under no tillage as a function of time, in two soil/climate conditions in São Paulo State, Brazil. The two sites measured approximately one hectare each and were cultivated with crop sequences which included corn, soybean, cotton, oats, black oats, wheat, rye, rice and green manure. Soil fertility, soil physical properties and crop yield were measured in a 10-m grid. The soils were a Dusky Red Latossol (Oxisol and a Red Yellow Latossol (Ultisol. Soil sampling was performed in each field every two years after harvesting of the summer crop. Crop yield was measured at the end of each crop cycle, in 2 x 2.5 m sub plots. Data were analysed using semivariogram analysis and kriging interpolation for contour map generation. Yield maps were constructed in order to visually compare the variability of yields, the variability of the yield components and related soil properties. The results show that the factors affecting the variability of crop yield varies from one crop to another. The changes in yield from one year to another suggest that the causes of variability may change with time. The changes with time for the cross semivariogram between phosphorus in leaves and soybean yield is another evidence of this result.

Methodology for setup and data processing of mobile air quality measurements to assess the spatial variability of concentrations in urban environments

International Nuclear Information System (INIS)

Van Poppel, Martine; Peters, Jan; Bleux, Nico

2013-01-01

A case study is presented to illustrate a methodology for mobile monitoring in urban environments. A dataset of UFP, PM 2.5 and BC concentrations was collected. We showed that repeated mobile measurements could give insight in spatial variability of pollutants at different micro-environments in a city. Streets of contrasting traffic intensity showed increased concentrations by a factor 2–3 for UFP and BC and by 2.5 . The first quartile (P25) of the mobile measurements at an urban background zone seems to be good estimate of the urban background concentration. The local component of the pollutant concentrations was determined by background correction. The use of background correction reduced the number of runs needed to obtain representative results. The results presented, are a first attempt to establish a methodology for setup and data processing of mobile air quality measurements to assess the spatial variability of concentrations in urban environments. -- Highlights: ► Mobile measurements are used to assess the variability of air pollutants in urban environments. ► PM 2.5 , BC and UFP concentrations are presented for zones with different traffic characteristics. ► A methodology for background correction based on the mobile measurements is presented. ► The background concentration is estimated as the 25th percentile of the urban background data. ► The minimum numbers of runs for a representative estimate is reduced after background correction. -- This paper shows that the spatial variability of air pollutants in an urban environment can be assessed by a mobile monitoring methodology including background correction

Spatial Variability of Sources and Mixing State of Atmospheric Particles in a Metropolitan Area.

Science.gov (United States)

Ye, Qing; Gu, Peishi; Li, Hugh Z; Robinson, Ellis S; Lipsky, Eric; Kaltsonoudis, Christos; Lee, Alex K Y; Apte, Joshua S; Robinson, Allen L; Sullivan, Ryan C; Presto, Albert A; Donahue, Neil M

2018-05-30

Characterizing intracity variations of atmospheric particulate matter has mostly relied on fixed-site monitoring and quantifying variability in terms of different bulk aerosol species. In this study, we performed ground-based mobile measurements using a single-particle mass spectrometer to study spatial patterns of source-specific particles and the evolution of particle mixing state in 21 areas in the metropolitan area of Pittsburgh, PA. We selected sampling areas based on traffic density and restaurant density with each area ranging from 0.2 to 2 km 2 . Organics dominate particle composition in all of the areas we sampled while the sources of organics differ. The contribution of particles from traffic and restaurant cooking varies greatly on the neighborhood scale. We also investigate how primary and aged components in particles mix across the urban scale. Lastly we quantify and map the particle mixing state for all areas we sampled and discuss the overall pattern of mixing state evolution and its implications. We find that in the upwind and downwind of the urban areas, particles are more internally mixed while in the city center, particle mixing state shows large spatial heterogeneity that is mostly driven by emissions. This study is to our knowledge, the first study to perform fine spatial scale mapping of particle mixing state using ground-based mobile measurement and single-particle mass spectrometry.

GPS receivers for georeferencing of spatial variability of soil attributes Receptores GPS para georreferenciamento da variabilidade espacial de atributos do solo

Directory of Open Access Journals (Sweden)

David L Rosalen

2011-12-01

Full Text Available The characterization of the spatial variability of soil attributes is essential to support agricultural practices in a sustainable manner. The use of geostatistics to characterize spatial variability of these attributes, such as soil resistance to penetration (RP and gravimetric soil moisture (GM is now usual practice in precision agriculture. The result of geostatistical analysis is dependent on the sample density and other factors according to the georeferencing methodology used. Thus, this study aimed to compare two methods of georeferencing to characterize the spatial variability of RP and GM as well as the spatial correlation of these variables. Sampling grid of 60 points spaced 20 m was used. For RP measurements, an electronic penetrometer was used and to determine the GM, a Dutch auger (0.0-0.1 m depth was used. The samples were georeferenced using a GPS navigation receiver, Simple Point Positioning (SPP with navigation GPS receiver, and Semi-Kinematic Relative Positioning (SKRP with an L1 geodetic GPS receiver. The results indicated that the georeferencing conducted by PPS did not affect the characterization of spatial variability of RP or GM, neither the spatial structure relationship of these attributes.A caracterização da variabilidade espacial dos atributos do solo é indispensável para subsidiar práticas agrícolas de maneira sustentável. A utilização da geoestatística para caracterizar a variabilidade espacial desses atributos, como a resistência mecânica do solo à penetração (RP e a umidade gravimétrica do solo (UG, é, hoje, prática usual na agricultura de precisão. O resultado da análise geoestatística é dependente da densidade amostral e de outros fatores, como o método de georreferencimento utilizado. Desta forma, o presente trabalho teve como objetivo comparar dois métodos de georreferenciamento para a caracterização da variabilidade espacial da RP e da UG, bem como a correlação espacial dessas vari

Comparison of Three Plot Selection Methods for Estimating Change in Temporally Variable, Spatially Clustered Populations.

Energy Technology Data Exchange (ETDEWEB)

Thompson, William L. [Bonneville Power Administration, Portland, OR (US). Environment, Fish and Wildlife

2001-07-01

Monitoring population numbers is important for assessing trends and meeting various legislative mandates. However, sampling across time introduces a temporal aspect to survey design in addition to the spatial one. For instance, a sample that is initially representative may lose this attribute if there is a shift in numbers and/or spatial distribution in the underlying population that is not reflected in later sampled plots. Plot selection methods that account for this temporal variability will produce the best trend estimates. Consequently, I used simulation to compare bias and relative precision of estimates of population change among stratified and unstratified sampling designs based on permanent, temporary, and partial replacement plots under varying levels of spatial clustering, density, and temporal shifting of populations. Permanent plots produced more precise estimates of change than temporary plots across all factors. Further, permanent plots performed better than partial replacement plots except for high density (5 and 10 individuals per plot) and 25% - 50% shifts in the population. Stratified designs always produced less precise estimates of population change for all three plot selection methods, and often produced biased change estimates and greatly inflated variance estimates under sampling with partial replacement. Hence, stratification that remains fixed across time should be avoided when monitoring populations that are likely to exhibit large changes in numbers and/or spatial distribution during the study period. Key words: bias; change estimation; monitoring; permanent plots; relative precision; sampling with partial replacement; temporary plots.

On the strong law of large numbers for $\\varphi$-subgaussian random variables

OpenAIRE

Zajkowski, Krzysztof

2016-01-01

For $p\\ge 1$ let $\\varphi_p(x)=x^2/2$ if $|x|\\le 1$ and $\\varphi_p(x)=1/p|x|^p-1/p+1/2$ if $|x|>1$. For a random variable $\\xi$ let $\\tau_{\\varphi_p}(\\xi)$ denote $\\inf\\{a\\ge 0:\\;\\forall_{\\lambda\\in\\mathbb{R}}\\; \\ln\\mathbb{E}\\exp(\\lambda\\xi)\\le\\varphi_p(a\\lambda)\\}$; $\\tau_{\\varphi_p}$ is a norm in a space $Sub_{\\varphi_p}=\\{\\xi:\\;\\tau_{\\varphi_p}(\\xi)1$) there exist positive constants $c$ and $\\alpha$ such that for every natural number $n$ the following inequality $\\tau_{\\varphi_p}(\\sum_{i=1...

Spatial generalised linear mixed models based on distances.

Science.gov (United States)

Melo, Oscar O; Mateu, Jorge; Melo, Carlos E

2016-10-01

Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time. © The Author(s) 2013.

Bayesian spatial prediction of the site index in the study of the Missouri Ozark Forest Ecosystem Project

Science.gov (United States)

Xiaoqian Sun; Zhuoqiong He; John Kabrick

2008-01-01

This paper presents a Bayesian spatial method for analysing the site index data from the Missouri Ozark Forest Ecosystem Project (MOFEP). Based on ecological background and availability, we select three variables, the aspect class, the soil depth and the land type association as covariates for analysis. To allow great flexibility of the smoothness of the random field,...

Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models

Directory of Open Access Journals (Sweden)

Hui Wang

2017-10-01

Full Text Available Achieving relatively high-accuracy short-term wind speed forecasting estimates is a precondition for the construction and grid-connected operation of wind power forecasting systems for wind farms. Currently, most research is focused on the structure of forecasting models and does not consider the selection of input variables, which can have significant impacts on forecasting performance. This paper presents an input variable selection method for wind speed forecasting models. The candidate input variables for various leading periods are selected and random forests (RF is employed to evaluate the importance of all variable as features. The feature subset with the best evaluation performance is selected as the optimal feature set. Then, kernel-based extreme learning machine is constructed to evaluate the performance of input variables selection based on RF. The results of the case study show that by removing the uncorrelated and redundant features, RF effectively extracts the most strongly correlated set of features from the candidate input variables. By finding the optimal feature combination to represent the original information, RF simplifies the structure of the wind speed forecasting model, shortens the training time required, and substantially improves the model’s accuracy and generalization ability, demonstrating that the input variables selected by RF are effective.

Cross over of recurrence networks to random graphs and random ...

Indian Academy of Sciences (India)

2017-01-27

Jan 27, 2017 ... that all recurrence networks can cross over to random geometric graphs by adding sufficient amount of noise to .... municative [19] or social [20], deviate from the random ..... He has shown that the spatial effects become.

Spatial variability of coastal wetland resilience to sea-level rise using Bayesian inference

Science.gov (United States)

Hardy, T.; Wu, W.

2017-12-01

The coastal wetlands in the Northern Gulf of Mexico (NGOM) account for 40% of coastal wetland area in the United States and provide various ecosystem services to the region and broader areas. Increasing rates of relative sea-level rise (RSLR), and reduced sediment input have increased coastal wetland loss in the NGOM, accounting for 80% of coastal wetland loss in the nation. Traditional models for predicting the impact of RSLR on coastal wetlands in the NGOM have focused on coastal erosion driven by geophysical variables only, and/or at small spatial extents. Here we developed a model in Bayesian inference to make probabilistic prediction of wetland loss in the entire NGOM as a function of vegetation productivity and geophysical attributes. We also studied how restoration efforts help maintain the area of coastal wetlands. Vegetation productivity contributes organic matter to wetland sedimentation and was approximated using the remotely sensed normalized difference moisture index (NDMI). The geophysical variables include RSLR, tidal range, river discharge, coastal slope, and wave height. We found a significantly positive relation between wetland loss and RSLR, which varied significantly at different river discharge regimes. There also existed a significantly negative relation between wetland loss and NDMI, indicating that in-situ vegetation productivity contributed to wetland resilience to RSLR. This relation did not vary significantly between river discharge regimes. The spatial relation revealed three areas of high RSLR but relatively low wetland loss; these areas were associated with wetland restoration projects in coastal Louisiana. Two projects were breakwater projects, where hard materials were placed off-shore to reduce wave action and promote sedimentation. And one project was a vegetation planting project used to promote sedimentation and wetland stabilization. We further developed an interactive web tool that allows stakeholders to develop similar wetland

Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery

Science.gov (United States)

Moharana, Shreedevi; Dutta, Subashisa

2016-12-01

Chlorophyll and nitrogen are the most essential parameters for paddy crop growth. Spectroradiometric measurements were collected at canopy level during critical growth period of rice. Chemical analysis was performed to quantify the total leaf content. By exploiting the ground based measurements, regression models were established for chlorophyll and nitrogen aimed indices with their corresponding crop growth variables. Vegetation index models were developed for mapping these parameters from Hyperion imagery in an agriculture system. It was inferred that the present Simple Ratio (SR) and Leaf Nitrogen Concentration (LNC) indices, which followed a linear and nonlinear relationship respectively, were completely different from published Tian et al. (2011). The nitrogen content varied widely from 1 to 4% and only 2 to 3% for paddy crop using present modified index models and Tian et al. (2011) respectively. The modified LNC index model performed better than the established Tian et al. (2011) model as far as estimated nitrogen content from Hyperion imagery was concerned. Furthermore, within the observed chlorophyll range obtained from the studied rice varieties grown in the rice agriculture system, the index models (LNC, OASVI, Gitelson, mSR and MTCI) performed well in the spatial distribution of rice chlorophyll content from Hyperion imagery. Spatial distribution of total chlorophyll content varied widely from 1.77 to 5.81 mg/g (LNC), 3.0 to 13 mg/g (OASVI), 0.5 to 10.43 mg/g (Gitelson), 2.18 to 10.61 mg/g (mSR) and 2.90 to 5.40 mg/g (MTCI). The spatial information of these parameters will help in proper nutrient management, yield forecasting, and will serve as inputs for crop growth and forecasting models for a precision rice agriculture system.

Spatial variability in degassing at Erebus volcano, Antarctica

Science.gov (United States)

Ilanko, Tehnuka; Oppenheimer, Clive; Kyle, Philip; Burgisser, Alain

2015-04-01

Erebus volcano on Ross Island, Antarctica, hosts an active phonolitic lava lake, along with a number of persistently degassing vents in its summit crater. Flank degassing also occurs through ice caves and towers. The longevity of the lake, and its stable convection, have been the subject of numerous studies, including Fourier transform infrared (FTIR) spectroscopy of the lava lake. Two distinct gas compositions were previously identified in the main lava lake plume (Oppenheimer et al., 2009; 2011): a persistent 'conduit' gas with a more oxidised signature, ascribed to degassing through a permeable magma conduit; and a H2O- and SO2- enriched 'lake' composition that increases and decreases cyclically due to shallow degassing of incoming magma batches. During the past decade of annual field seasons on Erebus, gas compositions have been measured through FTIR spectroscopy at multiple sites around Erebus volcano, including flank degassing through an ice cave (Warren Cave). We present measurements from four such vents, and compare their compositions to those emitted from the main lava lake. Summit degassing involves variable proportions of H2O, CO2, CO, SO2, HF, HCl, OCS. Cyclicity is evident in some summit vents, but with signatures indicative of shallower magmatic degassing than that of the lava lake. By contrast, flank degassing at Warren Cave is dominated by H2O, CO2, and CH4. The spatial variability in gas compositions within the summit crater suggests an alternative origin for 'conduit' and 'lake' degassing to previous models that assume permeability in the main conduit. Rather, the two compositions observed in main lake degassing may be a result of decoupled 'conduit' gas and pulses of magma rising through discrete fractures before combining in the lake floor or the main plume. Smaller vents around the crater thus emit isolated 'lake' or 'conduit' compositions while their combined signature is observed in the lava lake. We suggest that this separation between gas

Convolutions of Heavy Tailed Random Variables and Applications to Portfolio Diversification and MA(1) Time Series

NARCIS (Netherlands)

J.L. Geluk (Jaap); L. Peng (Liang); C.G. de Vries (Casper)

1999-01-01

textabstractThe paper characterizes first and second order tail behavior of convolutions of i.i.d. heavy tailed random variables with support on the real line. The result is applied to the problem of risk diversification in portfolio analysis and to the estimation of the parameter in a MA(1) model.

SPATIAL VARIABILITY AND VITALITY OF EPIGEOUS TERMITE MOUNDS IN PASTURES OF MATO GROSSO DO SUL, BRAZIL

Directory of Open Access Journals (Sweden)

Sandra Santana Lima

2015-02-01

Full Text Available Epigeous termite mounds are frequently observed in pasture areas, but the processes regulating their population dynamics are poorly known. This study evaluated epigeous termite mounds in cultivated grasslands used as pastures, assessing their spatial distribution by means of geostatistics and evaluating their vitality. The study was conducted in the Cerrado biome in the municipality of Rio Brilhante, Mato Grosso do Sul, Brazil. In two pasture areas (Pasture 1 and Pasture 2, epigeous mounds (nests were georeferenced and analyzed for height, circumference and vitality (inhabited or not. The area occupied by the mounds was calculated and termite specimens were collected for taxonomic identification. The spatial distribution pattern of the mounds was analyzed with geostatistical procedures. In both pasture areas, all epigeous mounds were built by the same species, Cornitermes cumulans. The mean number of mounds per hectare was 68 in Pasture 1 and 127 in Pasture 2, representing 0.4 and 1 % of the entire area, respectively. A large majority of the mounds were active (vitality, 91 % in Pasture 1 and 84 % in Pasture 2. A “pure nugget effect” was observed in the semivariograms of height and nest circumference in both pastures reflecting randomized spatial distribution and confirming that the distribution of termite mounds in pastures had a non-standard distribution.

Sub-hour solar data for power system modeling from static spatial variability analysis

Energy Technology Data Exchange (ETDEWEB)

Hummon, Marissa R.; Ibanez, Eduardo; Brinkman, Gregory; Lew, Debra [National Renewable Energy Lab. (NREL), Golden, CO (United States)

2012-07-01

High penetration renewable integration studies need high quality solar power data with spatial-temporal correlations that are representative of a real system. For instance, as additional solar power sites are added, the relative amount of variability should decrease due to spatial averaging of localized irradiance fluctuations. This presentation will summarize the research relating sequential point-source sub-hour global horizontal irradiance (GHI) values to static, spatially distributed GHI values. This research led to the development of an algorithm for generating coherent sub-hour datasets that span distances ranging from 10 km to 4,000 km. The algorithm, in brief, generates synthetic GHI values at an interval of one minute, for a specific location, using SUNY/Clean Power Research, satellite-derived, hourly irradiance values for the nearest grid cell to that location and grid cells within 40 km. During each hour, the observed GHI value for the grid cell of interest and the surrounding grid cells is related, via probability distributions, to one of live temporal cloud coverage classifications (class I, II, III, IV, V). Synthesis algorithms are used to select one-minute time step GHI values based on the classification of the grid cell of interest in a particular hour. Three primary statistical measures of the dataset are demonstrated: reduction in ramps as a function of aggregation; coherence of GHI values across sites ranging from 6 to 400 km apart over time scales from one minute to three hours; and ramp magnitude and duration distributions as a function of time of day and day of year. (orig.)

Aggregation-cokriging for highly multivariate spatial data

KAUST Repository

Furrer, R.; Genton, M. G.

2011-01-01

Best linear unbiased prediction of spatially correlated multivariate random processes, often called cokriging in geostatistics, requires the solution of a large linear system based on the covariance and cross-covariance matrix of the observations. For many problems of practical interest, it is impossible to solve the linear system with direct methods. We propose an efficient linear unbiased predictor based on a linear aggregation of the covariables. The primary variable together with this single meta-covariable is used to perform cokriging. We discuss the optimality of the approach under different covariance structures, and use it to create reanalysis type high-resolution historical temperature fields. © 2011 Biometrika Trust.

Aggregation-cokriging for highly multivariate spatial data

KAUST Repository

Furrer, R.

2011-08-26

Best linear unbiased prediction of spatially correlated multivariate random processes, often called cokriging in geostatistics, requires the solution of a large linear system based on the covariance and cross-covariance matrix of the observations. For many problems of practical interest, it is impossible to solve the linear system with direct methods. We propose an efficient linear unbiased predictor based on a linear aggregation of the covariables. The primary variable together with this single meta-covariable is used to perform cokriging. We discuss the optimality of the approach under different covariance structures, and use it to create reanalysis type high-resolution historical temperature fields. © 2011 Biometrika Trust.

Scales of snow depth variability in high elevation rangeland sagebrush

Science.gov (United States)

Tedesche, Molly E.; Fassnacht, Steven R.; Meiman, Paul J.

2017-09-01

In high elevation semi-arid rangelands, sagebrush and other shrubs can affect transport and deposition of wind-blown snow, enabling the formation of snowdrifts. Datasets from three field experiments were used to investigate the scales of spatial variability of snow depth around big mountain sagebrush ( Artemisia tridentata Nutt.) at a high elevation plateau rangeland in North Park, Colorado, during the winters of 2002, 2003, and 2008. Data were collected at multiple resolutions (0.05 to 25 m) and extents (2 to 1000 m). Finer scale data were collected specifically for this study to examine the correlation between snow depth, sagebrush microtopography, the ground surface, and the snow surface, as well as the temporal consistency of snow depth patterns. Variograms were used to identify the spatial structure and the Moran's I statistic was used to determine the spatial correlation. Results show some temporal consistency in snow depth at several scales. Plot scale snow depth variability is partly a function of the nature of individual shrubs, as there is some correlation between the spatial structure of snow depth and sagebrush, as well as between the ground and snow depth. The optimal sampling resolution appears to be 25-cm, but over a large area, this would require a multitude of samples, and thus a random stratified approach is recommended with a fine measurement resolution of 5-cm.

Effects of spatial heterogeneity on butterfly species richness in Rocky Mountain National Park, CO, USA

Science.gov (United States)

Kumar, S.; Simonson, S.E.; Stohlgren, T.J.

2009-01-01

We investigated butterfly responses to plot-level characteristics (plant species richness, vegetation height, and range in NDVI [normalized difference vegetation index]) and spatial heterogeneity in topography and landscape patterns (composition and configuration) at multiple spatial scales. Stratified random sampling was used to collect data on butterfly species richness from seventy-six 20 ?? 50 m plots. The plant species richness and average vegetation height data were collected from 76 modified-Whittaker plots overlaid on 76 butterfly plots. Spatial heterogeneity around sample plots was quantified by measuring topographic variables and landscape metrics at eight spatial extents (radii of 300, 600 to 2,400 m). The number of butterfly species recorded was strongly positively correlated with plant species richness, proportion of shrubland and mean patch size of shrubland. Patterns in butterfly species richness were negatively correlated with other variables including mean patch size, average vegetation height, elevation, and range in NDVI. The best predictive model selected using Akaike's Information Criterion corrected for small sample size (AICc), explained 62% of the variation in butterfly species richness at the 2,100 m spatial extent. Average vegetation height and mean patch size were among the best predictors of butterfly species richness. The models that included plot-level information and topographic variables explained relatively less variation in butterfly species richness, and were improved significantly after including landscape metrics. Our results suggest that spatial heterogeneity greatly influences patterns in butterfly species richness, and that it should be explicitly considered in conservation and management actions. ?? 2008 Springer Science+Business Media B.V.

Neutron Transport in Spatially Random Media: An Assessment of the Accuracy of First Order Smoothing

International Nuclear Information System (INIS)

Williams, M.M.R.

2000-01-01

A formalism has been developed for studying the transmission of neutrons through a spatially stochastic medium. The stochastic components are represented by absorbing plates of randomly varying strength and random position. This type of geometry enables the Feinberg-Galanin-Horning method to be employed and leads to the solution of a coupled set of linear equations for the flux at the plate positions. The matrix of the coefficients contains members that are random and these are solved by simulation. That is, the strength and plate positions are sampled from uniform distributions and the equations solved many times (in this case 10 5 simulations are carried out). Probability distributions for the plate transmission and reflection factors are constructed from which the mean and variance can be computed.These essentially exact solutions enable closure approximations to be assessed for accuracy. To this end, we have compared the mean and variance obtained from the first order smoothing approximation of Keller with the exact results and have found excellent agreement for the mean values but note deviations of up to 40% for the variance. Nevertheless, for the problems considered here, first order smoothing appears to be of practical value and is very efficient numerically in comparison with simulation

Temporal and spatial variability of frost-free seasons in the Great Lakes region of the United States

Science.gov (United States)

Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman; Jeffrey A. Andresen

2014-01-01

The frequency and timing of frost events and the length of the growing season are critical limiting factors in many human and natural ecosystems. This study investigates the temporal and spatial variability of the date of last spring frost (LSF), the date of first fall frost (FFF), and the length of the frost-free season (FFS) in the Great Lakes region of the United...

Temporal and spatial variability in the aviation NOx-related O3 impact

International Nuclear Information System (INIS)

Gilmore, Christopher K; Barrett, Steven R H; Koo, Jamin; Wang, Qiqi

2013-01-01

Aviation NO x emissions promote tropospheric ozone formation, which is linked to climate warming and adverse health effects. Modeling studies have quantified the relative impact of aviation NO x on O 3 in large geographic regions. As these studies have applied forward modeling techniques, it has not been possible to attribute O 3 formation to individual flights. Here we apply the adjoint of the global chemistry–transport model GEOS-Chem to assess the temporal and spatial variability in O 3 production due to aviation NO x emissions, which is the first application of an adjoint to this problem. We find that total aviation NO x emitted in October causes 40% more O 3 than in April and that Pacific aviation emissions could cause 4–5 times more tropospheric O 3 per unit NO x than European or North American emissions. Using this sensitivity approach, the O 3 burden attributable to 83 000 unique scheduled civil flights is computed individually. We find that the ten highest total O 3 -producing flights have origins or destinations in New Zealand or Australia. The top ranked O 3 -producing flights normalized by fuel burn cause 157 times more normalized O 3 formation than the bottom ranked ones. These results show significant spatial and temporal heterogeneity in environmental impacts of aviation NO x emissions. (letter)

Drop Spreading with Random Viscosity

Science.gov (United States)

Xu, Feng; Jensen, Oliver

2016-11-01

Airway mucus acts as a barrier to protect the lung. However as a biological material, its physical properties are known imperfectly and can be spatially heterogeneous. In this study we assess the impact of these uncertainties on the rate of spreading of a drop (representing an inhaled aerosol) over a mucus film. We model the film as Newtonian, having a viscosity that depends linearly on the concentration of a passive solute (a crude proxy for mucin proteins). Given an initial random solute (and hence viscosity) distribution, described as a Gaussian random field with a given correlation structure, we seek to quantify the uncertainties in outcomes as the drop spreads. Using lubrication theory, we describe the spreading of the drop in terms of a system of coupled nonlinear PDEs governing the evolution of film height and the vertically-averaged solute concentration. We perform Monte Carlo simulations to predict the variability in the drop centre location and width (1D) or area (2D). We show how simulation results are well described (at much lower computational cost) by a low-order model using a weak disorder expansion. Our results show for example how variability in the drop location is a non-monotonic function of the solute correlation length increases. Engineering and Physical Sciences Research Council.

Shade tree spatial structure and pod production explain frosty pod rot intensity in cacao agroforests, Costa Rica.

Science.gov (United States)

Gidoin, Cynthia; Avelino, Jacques; Deheuvels, Olivier; Cilas, Christian; Bieng, Marie Ange Ngo

2014-03-01

Vegetation composition and plant spatial structure affect disease intensity through resource and microclimatic variation effects. The aim of this study was to evaluate the independent effect and relative importance of host composition and plant spatial structure variables in explaining disease intensity at the plot scale. For that purpose, frosty pod rot intensity, a disease caused by Moniliophthora roreri on cacao pods, was monitored in 36 cacao agroforests in Costa Rica in order to assess the vegetation composition and spatial structure variables conducive to the disease. Hierarchical partitioning was used to identify the most causal factors. Firstly, pod production, cacao tree density and shade tree spatial structure had significant independent effects on disease intensity. In our case study, the amount of susceptible tissue was the most relevant host composition variable for explaining disease intensity by resource dilution. Indeed, cacao tree density probably affected disease intensity more by the creation of self-shading rather than by host dilution. Lastly, only regularly distributed forest trees, and not aggregated or randomly distributed forest trees, reduced disease intensity in comparison to plots with a low forest tree density. A regular spatial structure is probably crucial to the creation of moderate and uniform shade as recommended for frosty pod rot management. As pod production is an important service expected from these agroforests, shade tree spatial structure may be a lever for integrated management of frosty pod rot in cacao agroforests.

High-resolution spatial databases of monthly climate variables (1961-2010) over a complex terrain region in southwestern China