Environmental differences between two neighboring regions of Southern Spain

• Autorzy: Huete-Morales M.-D. , Quesada-Rubio J.-M. , Rosales-Moreno M.-J. , Navarrete-Alvarez E. , Del-Moral-Avila M.-J.
• Języki publikacji: EN

Abstrakty

EN

The aim of this paper is to identify the main factors that determine territorial differences between two large neighbouring provinces (Granada and Almería) that each occupy both sides of the Sierra Nevada mountain range in southern Spain. Our analysis produced a prediction model for riverside woodland cover, one of the variables that most strongly characterizes the differences between the two provinces. A general linear model was constructed for spatial analysis (spherical model with REML estimation), taking into account the location of the municipalities (longitude, latitude, and elevation) and other factors related to soil type, vegetation, or woodland cover. Interpolation of spatial data was performed by ordinary kriging. The predictions obtained over a grid of locations enabled patterns in each region to be derived and visualised, with very clear results. The pattern of riverside woodland cover is highly differentiated between the two provinces, despite their presenting adjoining areas.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2018
• Tom: 27
• Numer: 5
• Opis fizyczny: p.2071-2080,fig.,ref.

Twórcy

autor

Huete-Morales M.-D.

• Department of Statistics and Operational Research, Faculty of Labour Sciences, University of Granada, C/Rector Lopez Argueta, s/n. Granada, Spain

autor

Quesada-Rubio J.-M.

• Department of Statistics and Operational Research, Faculty of Labour Sciences, University of Granada, C/Rector Lopez Argueta, s/n. Granada, Spain

autor

Rosales-Moreno M.-J.

• Department of Statistic and Operational Research, Faculty of Sciences, University of Granada, Campus de Fuente Nueva, s/n. Granada, Spain

autor

Navarrete-Alvarez E.

• Department of Statistic and Operational Research, Faculty of Sciences, University of Granada, Campus de Fuente Nueva, s/n. Granada, Spain

autor

Del-Moral-Avila M.-J.

• Department of Statistic and Operational Research, Faculty of Sciences, University of Granada, Campus de Fuente Nueva, s/n. Granada, Spain

Bibliografia

• 1. ORTEGA M.T., MORALES C.G., LABAJO J.L. Contributions on changes in trends of climatic variables in the Spanish Central Plateau. Polígonos, 24, 43, 2013 [In Spanish] http://dx.doi.org/10.18002/pol.v0i24.841
• 2. MELENDEZ-PASTOR I., HERNÁNDEZ E.I., NAVARRO-PEDRENO J., GÓMEZ I. Socioeconomic factors influencing land cover changes in rural areas: the case of the Sierra de Albarracín (Spain). Applied Geography, 52, 34, 2014. http://dx.doi.org/10.1016/j.apgeog.2014.04.013
• 3. SOLÉ-SABARÍS L. Introduction. Spanish regions. In: Manuel de Terán, Luis Solé Sabarís y Juan Vilá Valentí. Regional Geography of Spain. Ed. Ariel: Barcelona, 1988 [In Spanish].
• 4. DANTÍN-CERECEDA J. Natural regions of Spain. CSIC, Inst. Juan Sebastián Elcano: Madrid, 1942 [In Spanish].
• 5. MERINO A., LÓPEZ A., HERMIDA L., SÁNCHEZ J.L., GARCÍA-ORTEGA E., GASCÓN E., FERNÁNDEZ-GONZÁLEZ S. Identification of drought phases in a 110-year record from Western Mediterranean basin: Trends, anomalies and periodicity analysis for Iberian Peninsula. Global and Planetary Change, 133, 96, 2015. http://dx.doi.org/10.1016/j.gloplacha.2015.08.007
• 6. VILLALOBOS MEGÍA M. Geology of the Arid Zone of Almería, South East Spain. Regional Ministry of Environment: Andalusia, 2003.
• 7. CRUZ-CÁRDENAS G., LÓPEZ-MATA L., ORTIZ-SOLORIO C.A., VILLASEÑOR J.A., ORTIZ E., SILVA J.T., ESTRADA-GODOY F. Interpolation of Mexican soil properties at a scale of 1:1,000,000. Geoderma, 213, 29, 2014. https://doi.org/10.1016/j.geoderma.2013.07.014.
• 8. TARASOV D.A., MEDVEDEV A.N., SERGEEVA.P., SHICHKIN A.V., BUEVICH A.G. A Hybrid Method for Assessment of Soil Pollutants Spatial Distribution. AIP Conference Proceedings 1863, 050015, 2017. http://dx.doi.org/10.1063/1.4992212
• 9. LOISEAU E., ROUX P., JUNQUA G., MAUREL P., BELLON-MAUREL V. Implementation of an adapted LCA framework to environmental assessment of a territory: important learning points from a French Mediterranean case study. Journal of Cleaner Production, 80, 17, 2014. http://dx.doi.org/10.1016/j.jclepro.2014.05.059
• 10. LÓPEZ-ONTIVEROS A. Geography of Andalusia. Ariel: Barcelona, 2003 [In Spanish].
• 11. INE. Intercensal population estimates, 2013. Spanish National Institute of Statistics. Available online: http://www.ine.es.
• 12. GÓMEZ-DÍAZ J. Territorial division of Spain. Provinces and judicial parties, 175 years. Toletum: Newsletter of the Royal Academy of Fine Arts and Historical Sciences of Toledo, 55, 151, 2014 [In Spanish].
• 13. FAULKNER H., RUÍZ J., ZUKOWSKYJ P., DOWNWARD S. Erosion risk associated with rapid and extensive agricultural clearances on dispersive materials in southeast Spain. Environmental Science & Policy, 6, 115, 2003. http://dx.doi.org/10.1016/S1462-9011(02)00126-0
• 14. SANZ-DE-GALDEANO C., LOPEZ-GARRIDO A.C. The nevado-filabride complex in the western part of Sierra de los Filabres (Betic Internal Zone), structure and lithologic succession. Boletín geológico y minero, 127 (4), 823, 2016.
• 15. SANZ-DE-GALDEANO C., LOPEZ-GARRIDO A.C. Transcurrent tectonics and melange in the Sierra Arana area (Betic Cordillera, NE of Granada). Estudios geológicos, 72 (2), e055, 2016.
• 16. GARCÍA-LLORENTE M., MARTÍN-LÓPEZ B., NUNES P.A., CASTRO A.J., MONTES C. A choice experiment study for land-use scenarios in semi-arid watershed environments. Journal of Arid Environments, 87, 219, 2012. http://dx.doi.org/10.1016/j.jaridenv.2012.07.015
• 17. KIM J. Crossing-over between land cover and land use: Exploring spatially varying relationships in two large US metropolitan areas. Applied Geography, 60, 37, 2015. http://dx.doi.org/10.1016/j.apgeog.2015.03.002
• 18. OLIVA M., MORENO I. Sierra Nevada, nexus between two teleconnection patterns: Nao and Wemo. In Regional Climate Change and its Impacts, series A, 6, 199, Spanish Climatology Association, Tarragona, Spain, 2008 [In Spanish].
• 19. RODRIGUEZ-GALIANO V., CHICA-OLMO M. Land cover change analysis of a Mediterranean area in Spain using different sources of data: Multi-seasonal Landsat images, land surface temperature, digital terrain models and texture. Applied Geography, 35, 208, 2012. http://dx.doi.org/10.1016/j.apgeog.2012.06.014
• 20. MUÑOZ-ROJAS M., DE LA ROSA D., ZAVALA,L.M., JORDÁN A., ANAYA-ROMERO M. Changes in land cover and vegetation carbon stocks in Andalusia, Southern Spain (1956-2007). Science of the Total Environment, 409, 2796, 2011. http://dx.doi.org/10.1016/j.scitotenv.2011.04.009
• 21. BERGHAENEL R.P. Föehm effect in the Catalan Pyrenees: general characteristics and a case study. Time and climate, 5 (10), 2014 [In Spanish].
• 22. PASCUAL R., CALLADO A. Mountain accidents associated with northern flows in the Mediterranean Pyrenees. Tethis, 7, 41, 2010.
• 23. INE. Statistical Yearbook of Spain: AEMET (Spanish State Meteorological Agency), 2013. Spanish National Institute of Statistics. Available online: http://www.ine.es.
• 24. WACKERNAGEL H. Multivariate geostatistics. 3rd Edition. Springer: Berlin, 2003.
• 25. SCHABENBERGER O., GOTWAY C.A. Statistical Methods for Spatial Data Analysis. Chapman & Hall/CRC Press: United State, 2005.
• 26. GAETAN C., GUYON X. Spatial statistics and modeling. Springer: New York, 2010.
• 27. JOURNEL A., HUIJBREGTS C. Mining Geostatistics, Academic Press: London, 1978.
• 28. ISAAKS E., SRISVASTAVA R. An Introduction to Applied Geostatistics, Oxford University Press: New York, 1989.
• 29. JOURNEL A. Fundamentals of geostatistics in five lessons. American Geophysical Union: Florida, 1989. http://dx.doi.org/10.1029/SC008
• 30. GOOVAERTS P. Geostatistics for Natural Resources Evaluation, Oxford University Press: New York, 1997.
• 31. DIGGLE P.J., TAWN J.A., MOYEED R.A. Model based geostatistics (with discussion). Applied Statistics, 47, 299, 1998. http://dx.doi.org/10.1111/1467-9876.00113
• 32. CRESSIE N. Statistics for Spatial Data, Wiley & Sons: New York, 2015.
• 33. COX D.R., MULLER H.D. The Theory of Stochastic Processes, Methuen: London, 1965.
• 34. SCHLATHER M. Introduction to positive definite functions and to unconditional simulation of random fields, Technical Report ST-99-10, 1999. Dept. Maths and Stats, Lancaster University, Lancaster, UK.
• 35. OLIVER M. The variogram and kriging. In Handbook of Applied Spatial Analysis. Springer-Verlag: Berlin, 2010. http://dx.doi.org/10.1007/978-3-642-03647-7_17.
• 36. DIGGLE P., MENEZES R., SU T. Geostatistical inference under preferential sampling. Journal of the Royal Statistical Society: Series C, 59 (2), 1912, 2010.
• 37. ZIMMERMAN D., ZIMMERMAN M. A comparison of spatial semivariogram estimators and corresponding kriging predictors, Technometrics, 33, 77, 1991. http://dx.doi.org/10.1080/00401706.1991.10484771
• 38. CRESSIE N. Fitting variogram models by weighted least squares, Journal of the International Association of Mathematical Geology, 17, 563, 1985. http://dx.doi.org/10.1007/BF01032109
• 39. MARCHANT B.P., LARK R.M. Robust estimation of the variogram by residual maximum likelihood. Geoderma, 140, 62, 2007.
• 40. PARDO-IGÚZQUIZA E. MLREML: a computer program for the inference of spatial covariance parameters by maximum likelihood and restricted maximum likelihood, Computers and Geosciences, 23, 153, 1997. http://dx.doi.org/10.1016/S0098-3004(97)85438-6
• 41. WEBSTER R., WELHAM S.J., POTTS J.M., OLIVER M.A. Estimating the spatial scale of regionalized variables by nested sampling, hierarchical analysis of variance and residual maximum likelihood. Computers and Geosciences, 32, 1320, 2006. http://dx.doi.org/10.1016/j. cageo.2005.12.002
• 42. HAINING R., KERRY R., OLIVER M. Geography, Spatial Data Analysis, and Geostatistics: An Overview. Geographical Analysis, 42 (1), 7, 2010. http://dx.doi.org/10.1111/j.1538-4632.2009.00780.x
• 43. DESASSIS N., RENARD D. Automatic variogram modeling by iterative least squares: univariate and multivariate cases. Mathematical Geosciences, 45, 453, 2013. http://dx.doi.org/10.1007/s11004-012-9434-1
• 44. SIMA: System of Multiterritorial Information of Andalusia, 2009. Institute of Statistics and Cartography of Andalusia. Available online: http://www.juntadeandalucia.es/institutodeestadisticaycartografia/sima/index2.htm
• 45. HUETE-MORALES M.D., QUESADA-RUBIO J.M., NAVARRETE-ÁLVAREZ E., ROSALES-MORENO M.J., DEL-MORAL-ÁVILA M.J. Geostatistical analysis of the causes of environmental noise in Spain. Environmental Engineering and Management Journal, 13 (10), 1535, 2014.
• 46. DICES. Cartographic directory of Spain, 2013 [In Spanish]. Available online: http://www.dices.net/poblaciones/4292.html.
• 47. ESRI. Map Service, 2013. Available online: http://www.arcgis.com/home/gallery.html.
• 48. R-PROJECT. The R-project for statistical computing, 2013. Available online: http://www.r-project.org/.
• 49. IVITS E., CHERLET M., MEHL W., SOMMER S. Estimating the ecological status and change of riparian zones in Andalusia assessed by multi-temporal AVHHR datasets. Ecological Indicators, 9, 422-431, 2009. http://dx.doi.org/10.1016/j.ecolind.2008.05.013
• 50. BOX G.E.P., COX D.R. An analysis of transformations. Journal of the Royal Statistical Society, Series B, 26, 211, 1964.
• 51. GARCÍA-BARRÓN L., AGUILERA M, SOUSA A. Evolution of annual rainfall irregularity in the southwest of the Iberian Peninsula. Theoretical and Applied Climatology, 103 (1), 13, 2011.
• 52. PEÑA-GALLARDO M., GÁMIZ-FORTIS S.R., CASTRO-DÍEZ Y., ESTEBAN-PARRA M.J. Comparative analysis of drought indices in Andalusia 1901-2012. Geographical Research Notebooks, 42 (1), 67, 2016 [In Spanish].
• 53. MENDOZA-FERNÁNDEZ A., PÉREZ-GARCÍA F., MARTÍNEZ-HERNÁNDEZ F., MEDINA-CAZORLA J., GARRIDO-BECERRA J., MERLO M., GUIRADO J., MOTA J. Threatened plants of arid ecosystems in the Mediterranean Basin: A case study of the south-eastern Iberian Peninsula. Oryx, 48 (4), 548, 2014. http://dx.doi.org/10.1017/S0030605313000495
• 54. CHILES J.P., DELFINER P. Geostatistics: modelling spatial uncertainty, John Wiley & Sons, Inc.: New York, 2009.
• 55. LARK R.M. Optimized spatial sampling of soil for estimation of the variogram by maximum likelihood. Geoderma, 105 (1-2), 49, 2002.
• 56. MARCHANT B.P., SABY N., LARK R., BELLAMY P, JOLIVET C., ARROUAYS D. Robust analysis of soil properties at the national scale: cadmium content of French soils. European Journal of Soil Science, 61, 144, 2010.http://dx.doi.org/10.1111/j.1365-2389.2009.01212.x
• 57. DIODATO N., CECCARELLI M. Multivariate indicator Kriging approach using a GIS to classify soil degradation for Mediterranean agricultural lands. Ecological Indicators, 4 (3), 177-187, 2004. http://dx.doi.org/10.1016/j.ecolind.2004.03.002.
• 58. PARDO-IGUZQUIZA E., CHICA-OLMO M., GARCIA-SOLDADO M.J., LUQUE-ESPINAR J.A. Using Semivariogram Parameter Uncertainty in Hydrogeological Applications, Ground Water, 47 (1), 25, 2009. http://dx.doi.org/10.1111/j.1745-6584.2008.00494.x
• 59. ANDERSON F. Multivariate Geostatistical Model for Groundwater Constituents in Texas. International Journal of Geosciences, 5 (13), 1609, 2014. http://dx.doi.org/10.4236/ijg.2014.513132
• 60. AMRI N., JEMAIN A., HASSAN W. Ordinary kriging base on OLS-WLS fitting semivariogram: Case of gold vein precipitation. In AIP Conference Proceedings. AIP, 1039, 2014.
• 61. FAWCETT L., WALSHAW D. Estimating the probability of simultaneous rainfall extremes within a region: a spatial approach. Journal of Applied Statistics, 41 (5), 959, 2014. http://dx.doi.org/10.1080/02664763.2013.856872
• 62. AROWOLO A., BHOWMIK A., QI W., DENG X. Comparison of spatial interpolation techniques to generate high-resolution climate surfaces for Nigeria. International Journal of Climatology, 2017. http://dx.doi.org/10.1002/joc.4990 [published online]

Identyfikatory

DOI

10.15244/pjoes/78625