Optimal design of rain gauge network in the Middle Yarra River catchment, Australia

Rainfall data are a fundamental input for effective planning, designing and operating of water resources projects. A well-designed rain gauge network is capable of providing accurate estimates of necessary areal average and/or point rainfall estimates at any desired ungauged location in a catchment. Increasing network density with additional rain gauge stations has been the main underlying criterion in the past to reduce error and uncertainty in rainfall estimates. However, installing and operation of additional stations in a network involves large cost and manpower. Hence, the objective of this study is to design an optimal rain gauge network in the Middle Yarra River catchment in Victoria, Australia. The optimal positioning of additional stations as well as optimally relocating of existing redundant stations using the kriging-based geostatistical approach was undertaken in this study. Reduction of kriging error was considered as an indicator for optimal spatial positioning of the stations. Daily rainfall records of 1997 (an El Niño year) and 2010 (a La Niña year) were used for the analysis. Ordinary kriging was applied for rainfall data interpolation to estimate the kriging error for the network. The results indicate that significant reduction in the kriging error can be achieved by the optimal spatial positioning of the additional as well as redundant stations. Thus, the obtained optimal rain gauge network is expected to be appropriate for providing high quality rainfall estimates over the catchment. The concept proposed in this study for optimal rain gauge network design through combined use of additional and redundant stations together is equally applicable to any other catchment

Semivariogram calculation optimization for object-oriented image classification

[EN] In this paper we propose and evaluate different mathematical parameters extracted from the experimental semivariogram for land use/land cover classification using high-resolution images and cadastral mapping limits for the definition of the objects of analysis. First, we describe the process of calculating the semivariogram from the gray level values in an image object. In order to optimize the computation time we present two pixel selection techniques that preserve the original shape of the semivariogram. Several parameters are then extracted from the semivariogram. Finally, we use various statistical techniques to select the most discriminant parameters. Last section shows the results obtained using aerial digital images of an agricultural area on the Mediterranean coast of Spain. The study of the practical application presented in this paper facilitates the understanding of the relationship between the behaviour of the experimental semivariogram and the variability of the intensity values in a digital image. In order to follow the development of this work, the reader should know some basis of classification methods and digital image processing techniques.[ES] En este trabajo se proponen y evalúan diferentes parámetros matemáticos extraídos del semivariograma experimental para la clasificación de los usos del suelo mediante imágenes de alta resolución, usando los límites catastrales para la definición de los objetos de análisis. En primer lugar, se describe el proceso de cálculo del semivariograma a partir de los valores de niveles de gris del objeto imagen. Con el fin de optimizar el tiempo de cálculo se presentan dos técnicas de selección de píxeles que conservan la forma original del semivariograma. A continuación se definen varios parámetros del semivariograma. Final- mente, se usan diferentes técnicas estadísticas para la selección de los parámetros más discriminantes. La última sección muestra los resultados obtenidos con las imágenes digitales aéreas de una zona agrícola en la costa mediterránea de España. El estudio de la aplicación práctica que se presenta facilita la comprensión de la relación entre el comportamiento del semivariograma experimental y la variabilidad de los valores de intensidad en una imagen digital. Con el fin de seguir el desarrollo de este trabajo, el lector debe conocer algunos métodos estadísticos de clasificación y algunas técnicas de procesamiento digital de imágenes.The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation and the FEDER in the framework of the Projects CGL2009-14220-C02-01 and CGL2010-19591/BTE.Balaguer-Beser, A.; Hermosilla, T.; Recio, J.; Ruiz, L. (2011). Semivariogram calculation optimization for object-oriented image classification. Modelling in Science Education and Learning. 4:91-104. https://doi.org/10.4995/msel.2011.3057SWORD911044Curran, P. J. (1988). The semivariogram in remote sensing: An introduction. Remote Sensing of Environment, 24(3), 493-507. doi:10.1016/0034-4257(88)90021-1J.P. Chilés, P. Delfinder, 1999, Geostatistics. Modeling Spatial Uncertainty, John Wiley and Sons, New York.P. Goovaerts, 1997, Geostatistics for Natural Resources Evaluation. Oxford University Press: New York.E.H. Isaaks, R.M. Srivastava, 1989, An introduction to applied geostatistics. Oxford. [10] D.K. McIver, M.A. Friedl, 2002, Using prior probabilities in decision tree classification of remotely sensed data. Remote Sensing of Environment 81, 253-261.M.J. Pyrcz, C.V. Deutsch, 2003, The Whole Story on the Hole Effect. In: Searston, S. (Eds.) Geostatistical Association of Australasia, Newsletter 18.J.R. Quinlan, 1993, C4.5: Programs For Machine Learning. Morgan Kaufmann, Los Altos.L.A. Ruiz, J.A. Recio, T. Hermosilla, 2007, Methods for automatic extraction of regularity patterns and its application to object-oriented image classification. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVI, Munich, Germany, 117-121.M. Story, R. G. Congalton, 1986, Accuracy assessment: a user's perspective, Photogram- metric Engineering and Remote Sensing, 52(3), 397-399

Tools for optimizing management of a spatially variable organic field

Geostatistical tools were used to estimate spatial relations between wheat yield and soil parameters under organic farming field conditions. Thematic maps of each factor were created as raster images in R software using kriging. The Geographic Resources Analysis Support System (GRASS) calculated the principal component analysis raster images for soil parameters and yield. The correlation between the raster arising from the PC1 of soil and yield parameters showed high linear correlation (r = 0.75) and explained 48.50% of the data variance. The data show that durum wheat yield is strongly affected by soil parameter variability, and thus, the average production can be substantially lower than its potential. Soil water content was the limiting factor to grain yield and not nitrate as in other similar studies. The use of precision agriculture tools helped reduce the level of complexity between the measured parameters by the grouping of several parameters and demonstrating that precision agriculture tools can be applied in small organic fields, reducing costs and increasing wheat yield. Consequently, site-specific applications could be expected to improve the yield without increasing excessively the cost for farmers and enhance environmental and economic benefits.Foundation for Science and Technology (Fundacao para a Ciencia e a Tecnologia), Portugal [SFRH/BD/8303/2002]; Research Center of Spatial and Organizational Dynamics (CIEO); Ministery of Science, Culture and Sport, Israel; Bundesmenisterium fuer Bildung and Forschung (BMBF

High-resolution truncated plurigaussian simulations for the characterization of heterogeneous formations

Integrating geological concepts, such as relative positions and proportions of the different lithofacies, is of highest importance in order to render realistic geological patterns. The truncated plurigaussian simulation method provides a way of using both local and conceptual geological information to infer the distributions of the facies and then those of hydraulic parameters. The method (Le Loc'h and Galli 1994) is based on the idea of truncating at least two underlying multi-Gaussian simulations in order to create maps of categorical variable. In this manuscript we show how this technique can be used to assess contaminant migration in highly heterogeneous media. We illustrate its application on the biggest contaminated site of Switzerland. It consists of a contaminant plume located in the lower fresh water Molasse on the western Swiss Plateau. The highly heterogeneous character of this formation calls for efficient stochastic methods in order to characterize transport processes.Comment: 12 pages, 9 figure

Singlet Magnetism in Heavy Fermions

We consider singlet magnetism for the uranium ions in UPt$_3$ and URu$_2$Si$_2$ assuming that time-reversal symmetry is broken for the {\em coherent state of intermediate valence}. The relative weight of the two involved configurations should be different for UPt$_3$ and URu$_2$Si$_2$. If in UPt$_3$ the configuration $5f^1$ on the U-ion prevails in the coherent state below the magnetic transition, the magnetic moment would vanish for the particular choice of the {\em ionic} wave function. In case of URu$_2$Si$_2$, the phase transition is non-magnetic in the first approximation -- the magnetic moment arises from a small admixture of a half-integer spin configuration.Comment: 12 pages, RevTex, no figures; Phys. Rev. Lett., to appea

Assessing the effect of clustered and biased multi-stage sampling

We propose a method for detecting biased multi-stage sampling of spatial data and a method to adjust for biased Clustering of samples. We assess the effect of these methods for the analysis of radioactivity contamination data from Rongelap island, with the scientific problem being the estimation of the maximum level of radioactivity over the island. These data were collected over a two-stage process of uniform and clustered samples, which may have an impact on conclusions from a standard analysis that does not account for either of these features.info:eu-repo/semantics/publishedVersio

Visualization of Na-diglyme co-intercalation induced few-layer graphene expansion and SEI formation using operando electrochemical atomic force microscopy

Diglyme solvated sodium-ion complexes enable the superfast co-intercalation of charge carriers (Na+) into graphite carbon interlayers,[1] providing unprecedented prospects for the application of low-cost graphite carbon as sodium-ion battery anode materials. Insights into this novel co-intercalation process are essential for enhancing the electrochemical performance of co-intercalation-based energy storage systems.[2, 3] Meanwhile, the paradox role of the co-existence of solid electrolyte interphase (SEI) and solvent co-intercalation behaviors needs to be further clarified.[4, 5] This presentation focuses on the real-space operando observation of the SEI formation, as well as Na-diglyme co-intercalation induced carbon-interlayers expansion in few-layer graphene as sodium anode electrodes. The few-layer graphene grown on the Ni current collector was patterned by Ar/O2 plasma to serve as a model anode electrode. The co-intercalation phenomenon was then directly observed by monitoring the interlayer spacing expansion using operando electrochemical atomic force microscopy (EC-AFM). The electrolyte decomposition was clearly observed on the few-layer graphene surfaces, and the anisotropic chemical components of SEI formed on graphite edge and basal planes were confirmed by XPS. The characterization results indicate that the SEI formed on the graphite edge planes cannot act as a physical ‘barrier’ to fully seal the edge sites and prevent the solvent co-intercalation into the carbon interlayers. This is due to the huge interlayer spacing expansion and contraction rate (300%) upon the intercalation/deintercalation of sodium-diglyme complex as confirmed by operando electrochemical EC-AFM characterisations

A MS-lesion pattern discrimination plot based on geostatistics

Introduction A geostatistical approach to characterize MS-lesion patterns based on their geometrical properties is presented. Methods A dataset of 259 binary MS-lesion masks in MNI space was subjected to directional variography. A model function was fit to express the observed spatial variability in x, y, z directions by the geostatistical parameters Range and Sill. Results Parameters Range and Sill correlate with MS-lesion pattern surface complexity and total lesion volume. A scatter plot of ln(Range) versus ln(Sill), classified by pattern anisotropy, enables a consistent and clearly arranged presentation of MS-lesion patterns based on geometry: the so-called MS-Lesion Pattern Discrimination Plot. Conclusions The geostatistical approach and the graphical representation of results are considered efficient exploratory data analysis tools for cross-sectional, follow-up, and medication impact analysis

Development of a hybrid model to interpolate monthly precipitation maps incorporating the orographic influence

[EN] This paper proposes an interpolation model for monthly rainfall in large areas of complex orography. It has been implemented in the Iberian Peninsula (continental territories of Spain and Portugal), Balearic and Canary Islands covering a territory of almost 600.000km(2). To do this a data set that comprises a total number of 11,822 monthly precipitation series has been created (11,042 provided by the Spanish Meteorological Agency and 780 provided by the National Water Resources Information System of the Portuguese Water Institute). The data set covers the period from October 1940 until September 2005. The interpolation model has been based on the assumption of two different components on monthly precipitation. The first component reflects local and seasonal characteristics and 24 different mean monthly precipitation maps (12) and SDs maps (12) compose it. It considers the varying influence of physiographic variables such as altitude and orientation. The second precipitation component reflects the synoptic pattern that dominated each month of the series and it is composed by series of anomalies of monthly precipitation (780). Anomalies have been interpolated by means of ordinary kriging once local spatial continuity was assumed. Gridded maps of each variable have been developed at 200m resolution following a hybrid methodology that implements two different interpolation techniques. The first technique applies a regression analysis to derive maps depending on altitude and orientation; the second one is a weighting technique to consider the non-linearity of the precipitation/altitude dependence. Cross validation has been applied to estimate the goodness of both techniques. Results show an average annual precipitation of 655mm/year. Although this figure is only 4% less than the estimate of MAGRAMA (2004), regional and local differences are highlighted when the spatial distribution is considered. The model constitutes a comprehensive implementation considering the availability of historical records and the need of avoiding slow calculations in large territories.Ministry of Economy, Industry and Competitiveness, Grant/Award Number: CGL2014-52571-RÁlvarez-Rodríguez, J.; Llasat, M.; Estrela Monreal, T. (2019). Development of a hybrid model to interpolate monthly precipitation maps incorporating the orographic influence. International Journal of Climatology. 39(10):3962-3975. https://doi.org/10.1002/joc.6051S396239753910AEMET.2011Atlas Climático Ibérico. (Iberian Climate Atlas) VV.AA. Agencia Estatal de Meteorología. Ministerio de Medio Ambiente. ISBN: 978‐84‐7837‐079‐5. 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