---
res:
  bibo_abstract:
  - 'The detection of a trend in a time series and the evaluation of its magnitude
    and statistical significance is an important task in geophysical research. This
    importance is amplified in climate change contexts, since trends are often used
    to characterize long‐term 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‐range 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‐Perron test
    and the Kwiatkowski‐Phillips‐Schmidt‐Shin test, are applied in order to test for
    trend stationary and difference stationary behavior in the temperature time series.
    Then long‐range 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‐Kendall, Cox‐Stuart
    and Spearman''s<jats:italic>ρ</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‐term features of the time series, and a deterministic
    positive trend is the most likely explanation.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Simone
      foaf_name: Fatichi, Simone
      foaf_surname: Fatichi
      foaf_workInfoHomepage: http://www.librecat.org/personId=cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6
  - foaf_Person:
      foaf_givenName: S. M.
      foaf_name: Barbosa, S. M.
      foaf_surname: Barbosa
  - foaf_Person:
      foaf_givenName: E.
      foaf_name: Caporali, E.
      foaf_surname: Caporali
  - foaf_Person:
      foaf_givenName: M. E.
      foaf_name: Silva, M. E.
      foaf_surname: Silva
  bibo_doi: 10.1029/2009jd011960
  bibo_issue: '18'
  bibo_volume: 114
  dct_date: 2009^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0148-0227
  - http://id.crossref.org/issn/ 2156-2202
  dct_language: eng
  dct_publisher: American Geophysical Union@
  dct_title: 'Deterministic versus stochastic trends: Detection and challenges@'
...