243 research outputs found

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

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    [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. Available at:http://www.aemet.es/documentos/es/conocermas/publicaciones/Atlas-climatologico/Atlas.pdf[Accessed 14th February 2018]Álvarez‐Rodríguez J.2011.Estimación de la distribución espacial de la precipitación en zonas montañosas mediante métodos geoestadísticos (Analysis of spatial distribution of precipitation in mountainous areas by means of geostatistical analysis). PhD Thesis. Polytechnic University of Madrid Higher Technical School of Civil EngineeringÁlvarez-Rodríguez, J., Llasat, M. C., & Estrela, T. (2017). Analysis of geographic and orographic influence in Spanish monthly precipitation. International Journal of Climatology, 37, 350-362. doi:10.1002/joc.5007Barros, A. P., Kim, G., Williams, E., & Nesbitt, S. W. (2004). Probing orographic controls in the Himalayas during the monsoon using satellite imagery. Natural Hazards and Earth System Sciences, 4(1), 29-51. doi:10.5194/nhess-4-29-2004Barstad, I., Grabowski, W. W., & Smolarkiewicz, P. K. (2007). Characteristics of large-scale orographic precipitation: Evaluation of linear model in idealized problems. Journal of Hydrology, 340(1-2), 78-90. doi:10.1016/j.jhydrol.2007.04.005Creutin, J. D., & Obled, C. (1982). Objective analyses and mapping techniques for rainfall fields: An objective comparison. Water Resources Research, 18(2), 413-431. doi:10.1029/wr018i002p00413Daly, C., Neilson, R. P., & Phillips, D. L. (1994). A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain. Journal of Applied Meteorology, 33(2), 140-158. doi:10.1175/1520-0450(1994)0332.0.co;2Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., … Pasteris, P. P. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 28(15), 2031-2064. doi:10.1002/joc.1688Daly, C., Slater, M. E., Roberti, J. A., Laseter, S. H., & Swift, L. W. (2017). High-resolution precipitation mapping in a mountainous watershed: ground truth for evaluating uncertainty in a national precipitation dataset. International Journal of Climatology, 37, 124-137. doi:10.1002/joc.4986Dhar, O. N., & Nandargi, S. (2004). Rainfall distribution over the Arunachal Pradesh Himalayas. Weather, 59(6), 155-157. doi:10.1256/wea.87.03Falivene, O., Cabrera, L., Tolosana-Delgado, R., & Sáez, A. (2010). Interpolation algorithm ranking using cross-validation and the role of smoothing effect. A coal zone example. Computers & Geosciences, 36(4), 512-519. doi:10.1016/j.cageo.2009.09.015Fiering, B., & Jackson, B. (1971). Synthetic Streamflows. Water Resources Monograph. doi:10.1029/wm001Gambolati, G., & Volpi, G. (1979). A conceptual deterministic analysis of the kriging technique in hydrology. Water Resources Research, 15(3), 625-629. doi:10.1029/wr015i003p00625Gómez-Hernández, J. J., Cassiraga, E. F., Guardiola-Albert, C., & Rodríguez, J. Á. (2001). Incorporating Information from a Digital Elevation Model for Improving the Areal Estimation of Rainfall. geoENV III — Geostatistics for Environmental Applications, 67-78. doi:10.1007/978-94-010-0810-5_6Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228(1-2), 113-129. doi:10.1016/s0022-1694(00)00144-xHanson, C. L. (1982). DISTRIBUTION AND STOCHASTIC GENERATION OF ANNUAL AND MONTHLY PRECIPITATION ON A MOUNTAINOUS WATERSHED IN SOUTHWEST IDAHO. Journal of the American Water Resources Association, 18(5), 875-883. doi:10.1111/j.1752-1688.1982.tb00085.xLloyd, C. D. (2005). Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain. Journal of Hydrology, 308(1-4), 128-150. doi:10.1016/j.jhydrol.2004.10.026Marquı́nez, J., Lastra, J., & Garcı́a, P. (2003). Estimation models for precipitation in mountainous regions: the use of GIS and multivariate analysis. Journal of Hydrology, 270(1-2), 1-11. doi:10.1016/s0022-1694(02)00110-5Martínez-Cob, A. (1996). Multivariate geostatistical analysis of evapotranspiration and precipitation in mountainous terrain. Journal of Hydrology, 174(1-2), 19-35. doi:10.1016/0022-1694(95)02755-6Mitáš, L., & Mitášová, H. (1988). General variational approach to the interpolation problem. Computers & Mathematics with Applications, 16(12), 983-992. doi:10.1016/0898-1221(88)90255-6Naoum, S., & Tsanis, I. K. (2004). Orographic Precipitation Modeling with Multiple Linear Regression. Journal of Hydrologic Engineering, 9(2), 79-102. doi:10.1061/(asce)1084-0699(2004)9:2(79)Ninyerola, M., Pons, X., & Roure, J. M. (2006). Monthly precipitation mapping of the Iberian Peninsula using spatial interpolation tools implemented in a Geographic Information System. Theoretical and Applied Climatology, 89(3-4), 195-209. doi:10.1007/s00704-006-0264-2Pebesma, E. J. (2004). Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30(7), 683-691. doi:10.1016/j.cageo.2004.03.012Rotunno, R., & Ferretti, R. (2001). Mechanisms of Intense Alpine Rainfall. Journal of the Atmospheric Sciences, 58(13), 1732-1749. doi:10.1175/1520-0469(2001)0582.0.co;2Singh, P., Ramasastri, K. S., & Kumar, N. (1995). Topographical Influence on Precipitation Distribution in Different Ranges of Western Himalayas. Hydrology Research, 26(4-5), 259-284. doi:10.2166/nh.1995.0015Tabios, G. Q., & Salas, J. D. (1985). A COMPARATIVE ANALYSIS OF TECHNIQUES FOR SPATIAL INTERPOLATION OF PRECIPITATION. Journal of the American Water Resources Association, 21(3), 365-380. doi:10.1111/j.1752-1688.1985.tb00147.xTHIESSEN, A. H. (1911). PRECIPITATION AVERAGES FOR LARGE AREAS. Monthly Weather Review, 39(7), 1082-1089. doi:10.1175/1520-0493(1911)392.0.co;2Tobin, C., Nicotina, L., Parlange, M. B., Berne, A., & Rinaldo, A. (2011). Improved interpolation of meteorological forcings for hydrologic applications in a Swiss Alpine region. Journal of Hydrology, 401(1-2), 77-89. doi:10.1016/j.jhydrol.2011.02.010Weber, D., & Englund, E. (1992). Evaluation and comparison of spatial interpolators. Mathematical Geology, 24(4), 381-391. doi:10.1007/bf00891270Weber, D. D., & Englund, E. J. (1994). Evaluation and comparison of spatial interpolators II. Mathematical Geology, 26(5), 589-603. doi:10.1007/bf02089243World Climate Programme.1985. World Meteorological Organization. Review of Requirements for Area‐Averaged Precipitation Data Surface‐Based and Space‐Based Estimation Techniques Space and Time Sampling Accurancy and Error; Data Exchange. Boulder Colorado EE.UU. 17–1

    Singlet Magnetism in Heavy Fermions

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    We consider singlet magnetism for the uranium ions in UPt3_3 and URu2_2Si2_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 UPt3_3 and URu2_2Si2_2. If in UPt3_3 the configuration 5f15f^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 URu2_2Si2_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

    A new generic open pit mine planning process with risk assessment ability

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    Conventionally, mining industry relies on a deterministic view, where a unique mine plan is determined based on a single resource model. A major shortfall of this approach is the inability to assess the risk caused by the well-known geological uncertainty, i.e. the in situ grade and tonnage variability of the mineral deposit. Despite some recent attempts in developing stochastic mine planning models which have demonstrated promising results, the industry still remains sceptical about this innovative idea. With respect to unbiased linear estimation, kriging is the most popular and reliable deterministic interpolation technique for resource estimation and it appears to remain its popularity in the near future. This paper presents a new systematic framework to quantify the risk of kriging-based mining projects due to the geological uncertainties. Firstly, conditional simulation is implemented to generate a series of equally-probable orebody realisations and these realisations are then compared with the kriged resource model to analyse its geological uncertainty. Secondly, a production schedule over the life of mine is determined based on the kriged resource model. Finally, risk profiles of that production schedule, namely ore and waste tonnage production, blending grade and Net Present Value (NPV), are constructed using the orebody realisations. The proposed model was applied on a multi-element deposit and the result demonstrates that that the kriging-based mine plan is unlikely to meet the production targets. Especially, the kriging-based mine plan overestimated the expected NPV at a magnitude of 6.70% to 7.34% (135 Mto151 M to 151 M). A new multivariate conditional simulation framework was also introduced in this paper to cope with the multivariate nature of the deposit. Although an iron ore deposit is used to prove the concepts, the method can easily be adapted to other kinds of mineral deposits, including surface coal mine

    Incorporating the geometry of dispersal and migration to understand spatial patterns of species distributions

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    Dispersal and migration can be important drivers of species distributions. Because the paths followed by individuals of many species are curvilinear, spatial statistical models based on rectilinear coordinates systems would fail to predict population connectivity or the ecological consequences of migration or species invasions. I propose that we view migration/dispersal as if organisms were moving along curvilinear geometrical objects called smooth manifolds. In that view, the curvilinear pathways become the ‘shortest realised paths’ arising from the necessity to minimise mortality risks and energy costs. One can then define curvilinear coordinate systems on such manifolds. I describe a procedure to incorporate manifolds and define appropriate coordinate systems, with focus on trajectories (1D manifolds), as parts of mechanistic ecological models. I show how a statistical method, known as ‘manifold learning’, enables one to define the manifold and the appropriate coordinate systems needed to calculate population connectivity or study the effects of migrations (e.g. in aquatic invertebrates, fish, insects and birds). This approach may help in the design of networks of protected areas, in studying the consequences of invasion, range expansions, or transfer of parasites/diseases. Overall, a geometrical view to animal movement gives a novel perspective to the understanding of the ecological role of dispersal and migration

    Annual accumulation for Greenland updated using ice core data developed during 2000-2006 and analysis of daily coastal meteorological data

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    An updated accumulation map for Greenland is presented on the basis of 39 new ice core estimates of accumulation, 256 ice sheet estimates from ice cores and snow pits used in previous maps, and reanalysis of time series data from 20 coastal weather stations. The period 1950-2000 is better represented by the data than are earlier periods. Ice-sheetwide accumulation was estimated based on kriging. The average accumulation (95 confidence interval, or ±2 times standard error) over the Greenland ice sheet is 30.0 ± 2.4 g cm -2 a-1, with the average accumulation above 2000-m elevation being essentially the same, 29.9 ± 2.2 g cm-2 a -1. At higher elevations the new accumulation map maintains the main features shown in previous maps. However, there are five coastal areas with obvious differences: southwest, northwest, and eastern regions, where the accumulation values are 20-50 lower than previously estimated, and southeast and northeast regions, where the accumulation values are 20-50 higher than previously estimated. These differences are almost entirely due to new coastal data. The much lower accumulation in the southwest and the much higher accumulation in the southeast indicated by the current map mean that long-term mass balance in both catchments is closer to steady state than previously estimated. However, uncertainty in these areas remains high owing to strong gradients in precipitation from the coast inland. A significant and sustained precipitation measurement program will be needed to resolve this uncertainty. Copyright 2009 by the American Geophysical Union

    A Geographer Looks at Spatial Information Theory

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    Abstract. Geographic information is defined as a subset of spatial information, specific to the spatiotemporal frame of the Earth’s surface. Thus geographic information theory inherits the results of spatial information theory, but adds results that reflect the specific properties of geographic information. I describe six general properties of geographic information, and show that in some cases specialization has assumed other properties that are less generally observed. A recognition of the distinction between geographic and spatial would allow geographic information theory to achieve greater depth and utility.

    Scenario of the spread of the invasive species Zaprionus indianus Gupta, 1970 (Diptera, Drosophilidae) in Brazil

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    Zaprionus indianus was first recorded in Brazil in 1999 and rapidly spread throughout the country. We have obtained data on esterase loci polymorphisms (Est2 and Est3), and analyzed them, using Landscape Shape Interpolation and the Monmonier Maximum Difference Algorithm to discover how regional invasion occurred. Hence, it was apparent that Z. indianus, after first arriving in São Paulo state, spread throughout the country, probably together with the transportation of commercial fruits by way of the two main Brazilian freeways, BR 153, to the south and the surrounding countryside, and the BR 116 along the coast and throughout the north-east

    Effects of Particulate Air Pollution on Cardiovascular Health: A Population Health Risk Assessment

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    Particulate matter (PM) air pollution is increasingly recognized as an important and modifiable risk factor for adverse health outcomes including cardiovascular disease (CVD). However, there are still gaps regarding large population risk assessment. Results from the nationwide Behavioral Risk Factor Surveillance System (BRFSS) were used along with air quality monitoring measurements to implement a systematic evaluation of PM-related CVD risks at the national and regional scales. CVD status and individual-level risk factors were collected from more than 500,000 BRFSS respondents across 2,231 contiguous U.S. counties for 2007 and 2009. Chronic exposures to PM pollutants were estimated with spatial modeling from measurement data. CVD outcomes attributable to PM pollutants were assessed by mixed-effects logistic regression and latent class regression (LCR), with adjustment for multicausality. There were positive associations between CVD and PM after accounting for competing risk factors: the multivariable-adjusted odds for the multiplicity of CVD outcomes increased by 1.32 (95% confidence interval: 1.23–1.43) and 1.15 (1.07–1.22) times per 10 µg/m3 increase in PM2.5 and PM10 respectively in the LCR analyses. After controlling for spatial confounding, there were moderate estimated effects of PM exposure on multiple cardiovascular manifestations. These results suggest that chronic exposures to ambient particulates are important environmental risk factors for cardiovascular morbidity
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