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Rainfall statistics, stationarity, and climate change.

Fubao Sun1,2,3,4,5, Michael L Roderick3,4,6, Graham D Farquhar7,4

  • 1Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.

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|February 22, 2018
PubMed
Summary
This summary is machine-generated.

Assessing hydrologic and climatic time series changes is crucial. While 14% of land shows significant precipitation changes, most areas exhibit stable behavior, highlighting the need to account for natural variability.

Keywords:
precipitationstationarityvariance

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Area of Science:

  • Climate Science
  • Hydrology
  • Time Series Analysis

Background:

  • Growing research interest in detecting changes in hydrologic and climatic time series.
  • Stationarity assessment using autocorrelation function is not yet common in hydrology and climate.

Purpose of the Study:

  • To assess stationarity and detect changes in global precipitation time series.
  • To evaluate the significance of recent precipitation changes considering natural variability.

Main Methods:

  • Utilized a global land-based gridded annual precipitation (P) database (1940-2009).
  • Applied autocorrelation function to assess stationarity.
  • Analyzed changes in observed P over the most recent decade.

Main Results:

  • Lag 1 autocorrelation coefficient was statistically significant (implying nonstationarity) at 14% of the global land surface (90% confidence).
  • Approximately 76% of the global land surface showed stationary behavior (little or no change).
  • Changes in observed P for 84% of grid boxes were within the bounds of no significant change at the 90% confidence interval.

Conclusions:

  • Emphasized the importance of adequately accounting for natural variability when assessing hydrologic and climatic change.
  • Results suggest that while some areas exhibit nonstationary precipitation patterns, a significant portion remains stable.