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

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

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dc.contributor.author Álvarez-Rodríguez, J. es_ES
dc.contributor.author Llasat, M.C. es_ES
dc.contributor.author Estrela Monreal, Teodoro es_ES
dc.date.accessioned 2020-03-12T06:51:48Z
dc.date.available 2020-03-12T06:51:48Z
dc.date.issued 2019-08 es_ES
dc.identifier.issn 0899-8418 es_ES
dc.identifier.uri http://hdl.handle.net/10251/138743
dc.description.abstract [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. es_ES
dc.description.sponsorship Ministry of Economy, Industry and Competitiveness, Grant/Award Number: CGL2014-52571-R es_ES
dc.language Inglés es_ES
dc.publisher John Wiley & Sons es_ES
dc.relation.ispartof International Journal of Climatology es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Hybrid method es_ES
dc.subject Interpolation es_ES
dc.subject Monthly precipitation es_ES
dc.subject Orography es_ES
dc.subject Spain es_ES
dc.subject.classification INGENIERIA HIDRAULICA es_ES
dc.title Development of a hybrid model to interpolate monthly precipitation maps incorporating the orographic influence es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1002/joc.6051 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//CGL2014-52571-R/ES/ANALISIS HOLISTICO DEL IMPACTO DE LAS PRECIPITACIONES EXTREMAS E INUNDACIONES Y SU INTRODUCCION EN ESCENARIOS FUTUROS. APLICACION A LAS ESTRATEGIAS DE ADAPTACION Y RESILIENCIA/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient es_ES
dc.description.bibliographicCitation Álvarez-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.6051 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1002/joc.6051 es_ES
dc.description.upvformatpinicio 3962 es_ES
dc.description.upvformatpfin 3975 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 39 es_ES
dc.description.issue 10 es_ES
dc.relation.pasarela S\398672 es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.description.references AEMET.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] es_ES
dc.description.references 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 Engineering es_ES
dc.description.references Álvarez-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.5007 es_ES
dc.description.references Barros, 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-4-29-2004 es_ES
dc.description.references Barstad, 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.2007.04.005 es_ES
dc.description.references Creutin, 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/wr018i002p00413 es_ES
dc.description.references Daly, 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)033<0140:astmfm>2.0.co;2 es_ES
dc.description.references Daly, 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.1688 es_ES
dc.description.references Daly, 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.4986 es_ES
dc.description.references Dhar, O. N., & Nandargi, S. (2004). Rainfall distribution over the Arunachal Pradesh Himalayas. Weather, 59(6), 155-157. doi:10.1256/wea.87.03 es_ES
dc.description.references Falivene, 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.2009.09.015 es_ES
dc.description.references Fiering, B., & Jackson, B. (1971). Synthetic Streamflows. Water Resources Monograph. doi:10.1029/wm001 es_ES
dc.description.references Gambolati, G., & Volpi, G. (1979). A conceptual deterministic analysis of the kriging technique in hydrology. Water Resources Research, 15(3), 625-629. doi:10.1029/wr015i003p00625 es_ES
dc.description.references Gómez-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_6 es_ES
dc.description.references Goovaerts, 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-x es_ES
dc.description.references Hanson, 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.x es_ES
dc.description.references Lloyd, 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.2004.10.026 es_ES
dc.description.references Marquı́nez, 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)00110-5 es_ES
dc.description.references Martínez-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)02755-6 es_ES
dc.description.references Mitáš, 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)90255-6 es_ES
dc.description.references Naoum, 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) es_ES
dc.description.references 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/s00704-006-0264-2 es_ES
dc.description.references Pebesma, E. J. (2004). Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30(7), 683-691. doi:10.1016/j.cageo.2004.03.012 es_ES
dc.description.references Rotunno, R., & Ferretti, R. (2001). Mechanisms of Intense Alpine Rainfall. Journal of the Atmospheric Sciences, 58(13), 1732-1749. doi:10.1175/1520-0469(2001)058<1732:moiar>2.0.co;2 es_ES
dc.description.references Singh, 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.1995.0015 es_ES
dc.description.references Tabios, 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.x es_ES
dc.description.references THIESSEN, A. H. (1911). PRECIPITATION AVERAGES FOR LARGE AREAS. Monthly Weather Review, 39(7), 1082-1089. doi:10.1175/1520-0493(1911)39<1082b:pafla>2.0.co;2 es_ES
dc.description.references Tobin, 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.2011.02.010 es_ES
dc.description.references Weber, D., & Englund, E. (1992). Evaluation and comparison of spatial interpolators. Mathematical Geology, 24(4), 381-391. doi:10.1007/bf00891270 es_ES
dc.description.references Weber, D. D., & Englund, E. J. (1994). Evaluation and comparison of spatial interpolators II. Mathematical Geology, 26(5), 589-603. doi:10.1007/bf02089243 es_ES
dc.description.references World 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. 17–19 es_ES
dc.subject.ods 13.- Tomar medidas urgentes para combatir el cambio climático y sus efectos es_ES


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