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This importance is amplified in climate change contexts, since trends are often used to characterize long\u2010term climate variability and to quantify the magnitude and the statistical significance of changes in climate time series, both at global and local scales. Recent studies have demonstrated that the stochastic behavior of a time series can change the statistical significance of a trend, especially if the time series exhibits long\u2010range dependence. The present study examines the trends in time series of daily average temperature recorded in 26 stations in the Tuscany region (Italy). In this study a new framework for trend detection is proposed. First two parametric statistical tests, the Phillips\u2010Perron test and the Kwiatkowski\u2010Phillips\u2010Schmidt\u2010Shin test, are applied in order to test for trend stationary and difference stationary behavior in the temperature time series. Then long\u2010range dependence is assessed using different approaches, including wavelet analysis, heuristic methods and by fitting fractionally integrated autoregressive moving average models. The trend detection results are further compared with the results obtained using nonparametric trend detection methods: Mann\u2010Kendall, Cox\u2010Stuart and Spearman's<jats:italic>\u03c1<\/jats:italic>tests. This study confirms an increase in uncertainty when pronounced stochastic behaviors are present in the data. Nevertheless, for approximately one third of the analyzed records, the stochastic behavior itself cannot explain the long\u2010term features of the time series, and a deterministic positive trend is the most likely explanation.<\/jats:p>","DOI":"10.1029\/2009jd011960","type":"journal-article","created":{"date-parts":[[2009,9,28]],"date-time":"2009-09-28T20:19:04Z","timestamp":1254169144000},"source":"Crossref","is-referenced-by-count":74,"title":["Deterministic versus stochastic trends: Detection and challenges"],"prefix":"10.1029","volume":"114","author":[{"given":"S.","family":"Fatichi","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering University of Firenze Florence Italy"}]},{"given":"S. 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