Articles | Volume 10, issue 11
Nat. Hazards Earth Syst. Sci., 10, 2429–2440, 2010

Special issue: 11th Plinius Conference on Mediterranean Storms

Nat. Hazards Earth Syst. Sci., 10, 2429–2440, 2010

Research article 30 Nov 2010

Research article | 30 Nov 2010

Investigating annual and monthly trends in precipitation structure: an overview across Portugal

M. I. P. de Lima3,1, S. C. P. Carvalho3,2, and J. L. M. P. de Lima3,2 M. I. P. de Lima et al.
  • 1Department of Forest Resources, Coimbra College of Agriculture, Polytechnic Institute of Coimbra, Bencanta, 3040-316 Coimbra, Portugal
  • 2Department of Civil Engineering, Faculty of Science and Technology, Campus II – University of Coimbra, Rua Luís Reis Santos, 3030-788 Coimbra, Portugal
  • 3IMAR – Marine and Environmental Research Centre, Department of Life Sciences, Faculty of Science and Technology, University of Coimbra, 3004-517 Coimbra, Portugal

Abstract. This work investigates recent changes in precipitation patterns manifested in long annual and monthly precipitation time series recorded in Portugal. The dataset comprises records from 14 meteorological stations scattered over mainland Portugal and the Portuguese North Atlantic Islands of Madeira and Azores; some of the time series date back to the 19th century. The data were tested for trends using the Mann-Kendall non-parametric test and Sen's non-parametric method, searching both for full monotonic trends over the record period and for partial trends. Results provide no evidence for rejecting the null hypothesis of no trend in annual precipitation, when a monotonic linear model was used. Nevertheless, the analyses of 50 years' moving averages showed an increase over time, in the recent past, for many of the series in mainland Portugal and the Islands. For the longest time series this behaviour was preceded by a decrease over time. The analyses of partial trends in the time series suggested a sequence of alternately decreasing and increasing trends in annual and monthly precipitation, which are sometimes statistically significant. The trend changing points were identified.