NHESS Natural Hazards and Earth System Sciences NHESS Nat. Hazards Earth Syst. Sci. 1684-9981 Copernicus Publications Göttingen, Germany 10.5194/nhess-8-763-2008 Using stochastic space-time models to map extreme precipitation in southern Portugal Costa A. C. 1 Durão R. 2 Pereira M. J. 2 Soares A. 2 ISEGI, Universidade Nova de Lisboa, Lisbon, Portugal CERENA, Instituto Superior Técnico, Lisbon, Portugal 30 07 2008 8 4 763 773 Copyright: © 2008 A. C. Costa et al. 2008 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://nhess.copernicus.org/articles/8/763/2008/nhess-8-763-2008.html The full text article is available as a PDF file from https://nhess.copernicus.org/articles/8/763/2008/nhess-8-763-2008.pdf

The topographic characteristics and spatial climatic diversity are significant in the South of continental Portugal where the rainfall regime is typically Mediterranean. Direct sequential cosimulation is proposed for mapping an extreme precipitation index in southern Portugal using elevation as auxiliary information. The analysed index (R5D) can be considered a flood indicator because it provides a measure of medium-term precipitation total. The methodology accounts for local data variability and incorporates space-time models that allow capturing long-term trends of extreme precipitation, and local changes in the relationship between elevation and extreme precipitation through time. Annual gridded datasets of the flood indicator are produced from 1940 to 1999 on 800 m×800 m grids by using the space-time relationship between elevation and the index. Uncertainty evaluations of the proposed scenarios are also produced for each year. The results indicate that the relationship between elevation and extreme precipitation varies locally and has decreased through time over the study region. In wetter years the flood indicator exhibits the highest values in mountainous regions of the South, while in drier years the spatial pattern of extreme precipitation has much less variability over the study region. The uncertainty of extreme precipitation estimates also varies in time and space, and in earlier decades is strongly dependent on the density of the monitoring stations network. The produced maps will be useful in regional and local studies related to climate change, desertification, land and water resources management, hydrological modelling, and flood mitigation planning.

 References Boer, E. P. J, de Beurs, K. M., and Hartkamp, A. D.: Kriging and thin plate splines for mapping climate variables, Int. J. Appl. Earth Obs., 3(2), 146–154, 2001. Bourennane, H., King, D., Couturier, A., Nicoullaud, B., Mary, B., and Richard, G.: Uncertainty assessment of soil water content spatial patterns using geostatistical simulations: An empirical comparison of a simulation accounting for single attribute and a simulation accounting for secondary information, Ecol. Model., 205, 323–335, 2007. Brunsdon, C., Mcclatchey, J., and Unwin, D. J.: Spatial variations in the average rainfall-altitude relationship in Great Britain: an approach using geographically weighted regression, Int. J. Climatol., 21, 4, 455–466, 2001. Corte-Real, J., Qian, B., and Xu, H.: Regional climate change in Portugal: precipitation variability associated with large-scale atmospheric circulation, Int. J. Climatol., 18, 619–635, 1998. Costa, A. C. and Soares, A.: Identification of inhomogeneities in precipitation time series using SUR models and the Ellipse test, in: Proceedings of Accuracy 2006 – 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, edited by: Caetano, M. and Painho, M., Instituto Geográfico Português, 419–428, 2006. Costa, A. C., Negreiros, J., and Soares, A.: Identification of inhomogeneities in precipitation time series using stochastic simulation, in: geoENV VI – Geostatistics for Environmental Applications, edited by: Soares, A., Pereira, M. J., and Dimitrakopoulos, R., Springer, 15, 275–282, 2008. Cubasch, U., von Storch, H., Waszkewitz, J., and Zorita, E.: Estimates of climate change in Southern Europe derived from dynamical climate model output, Clim. Res., 7, 129–149, 1996. Daly, C., Neilson, R. P., and Phillips, D. L.: A statistical-topographic model for mapping climatological precipitation over mountainous terrain, J. Appl. Meteorol., 33, 140–157, 1994. Daly, C.: Guidelines for assessing the suitability of spatial climate data sets, Int. J. Climatol., 26(6), 707–721, 2006. Diodato, N.: The influence of topographic co-variables on the spatial variability of precipitation over small regions of complex terrain, Int. J. Climatol., 25(3), 351–363, 2005. Faulkner, D. S. and Prudhomme, C.: Mapping an index of extreme rainfall across the UK, Hydrol. Earth Syst. Sc., 2, 183–194, 1998. Fragoso, M. and Gomes, P. T.: Classification of daily abundant rainfall patterns and associated large-scale atmospheric circulation types in Southern Portugal, Int. J. Climatol., 28(4), 537–544, https://doi.org/10.1002/joc.1564, 2008. Franco, C., Soares, A., and Delgado, J.: Geostatistical modelling of heavy metal contamination in the topsoil of Guadiamar river margins (S Spain) using a stochastic simulation technique, Geoderma, 136, 852–864, 2006. Frich, P., Alexander, L. V., Della-Marta, P., Gleason, B., Haylock, M., Klein Tank, A. M. G., and Peterson, T.: Observed coherent changes in climatic extremes during the second half of the twentieth century, Climate Res., 19(3), 193–212, 2002. Goodess, C. M. and Jones, P. D.: Links between circulation and changes in the characteristics of Iberian rainfall, Int. J. Climatol., 22(13), 1593–1615, 2002. Goovaerts, P.: Geostatistics for Natural Resources Evaluation. Applied Geostatistics Series, Oxford University Press, New York, Oxford, 1997. Goovaerts, P.: Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall, J. Hydrol., 228, 113–129, 2000. Haberlandt, U.: Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event, J. Hydrol., 332, 144–157, 2007. Hundecha, Y. and Bárdossy, A.: Trends in daily precipitation and temperature extremes across western Germany in the second half of the 20th century, Int. J. Climatol., 25(9), 1189–1202, 2005. Hutchinson, M. F.: Interpolating mean rainfall using thin plate smoothing splines, Int. J. Geogr. Inf. Syst., 9, 385–403, 1995. Isaaks, E. H. and Srivastava, R. M.: An Introduction to Applied Geostatistics, Oxford University Press, New York, Oxford, 1989. Johansson, B. and Chen, D.: The influence of wind and topography on precipitation distribution in Sweden: statistical analysis and modelling, Int. J. Climatol., 23(12), 1523–1535, 2003. Kostopoulou, E. and Jones, P. D.: Assessment of climate extremes in the Eastern Mediterranean, Meteorol. Atmos. Phys., 89, 69–85, 2005. Lloyd, C. D.: Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain, J. Hydrol., 308, 128–150, 2005. Mart\'inez-Cob, A.: Multivariate geostatistical analysis of evapotranspiration and precipitation in mountainous terrain, J. Hydrol., 174, 19–35, 1996. Perry, M. and Hollis, D.: The generation of monthly gridded datasets for a range of climatic variables over the UK, Int. J. Climatol., 25(8), 1041–1054, 2005. Prudhomme, C. and Reed, D. W.: Relationships between extreme daily precipitation and topography in a mountainous region: A case study in Scotland, Int. J. Climatol., 18(13), 1439–1453, 1998. Prudhomme, C. and Reed, D. W.: Mapping extreme rainfall in a mountainous region using geostatistical techniques: a case study in Scotland, Int. J. Climatol., 19(12), 1337–1356, 1999. Ramos, C. and Reis, E.: Floods in southern Portugal: their physical and human causes, impacts and human response, Mitigation and Adaptation Strategies for Global Change, 7(3), 267–284, 2002. Scaife, A. A., Folland, C. K., Alexander, L. V., Moberg, A., Knight, J. R.: European climate extremes and the North Atlantic Oscillation, J. Climate, 21, 72–83, 2008. Smith, R. B. and Barstad, I.: A linear theory of orographic precipitation, J. Atmos. Sci., 61(12), 1377–1391, 2004. Soares, A.: Direct Sequential Simulation and Cosimulation, Math. Geol., 33, 911–926, 2001. Trigo, R. M. and DaCamara, C.: Circulation Weather Types and their impact on the precipitation regime in Portugal, Int. J. Climatol., 20(13), 1559–1581, 2000. Trigo, R. M., Pozo-Vázquez, D., Osborn, T. J., Castro-D\'iez, Y., Gámiz-Fortis, S., and Esteban-Parra, M. J.: North Atlantic Oscillation influence on precipitation, river flow and water resources in the Iberian Peninsula, Int. J. Climatol., 24(8), 925–944, 2004. Vicente-Serrano, S. M. and Cuadrat-Prats, J. M.: Trends in drought intensity and variability in the middle Ebro valley (NE of the Iberian peninsula) during the second half of the twentieth century, Theor. Appl. Climatol., 88, 247–258, 2007. Wijngaard, J. B., Klein Tank, A. M. G., and Können, G. P.: Homogeneity of 20th century European daily temperature and precipitation series, Int. J. Climatol., 23, 679–692, 2003. Willmott, C. J. and Matsuura, K.: On the use of dimensioned measures of error to evaluate the performance of spatial interpolators, Int. J. Geogr. Inf. Sci., 20(1), 89–102, 2006.