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Climate 2018, 6(3), 75;

Spatial and Temporal Rainfall Variability over the Mountainous Central Pindus (Greece)

Faculty of Forestry and Natural Environment, Aristotle University of Thessaloniki, Laboratory of Mountainous Water Management and Control, 54124 Thessaloniki, Greece
Author to whom correspondence should be addressed.
Received: 19 July 2018 / Revised: 2 September 2018 / Accepted: 5 September 2018 / Published: 6 September 2018
(This article belongs to the Special Issue Climate Variability and Change in the 21th Century)
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In this study, the authors evaluated the spatial and temporal variability of rainfall over the central Pindus mountain range. To accomplish this, long-term (1961–2016) monthly rainfall data from nine rain gauges were collected and analyzed. Seasonal and annual rainfall data were subjected to Mann–Kendall tests to assess the possible upward or downward statistically significant trends and to change-point analyses to detect whether a change in the rainfall time series mean had taken place. Additionally, Sen’s slope method was used to estimate the trend magnitude, whereas multiple regression models were developed to determine the relationship between rainfall and geomorphological factors. The results showed decreasing trends in annual, winter, and spring rainfalls and increasing trends in autumn and summer rainfalls, both not statistically significant, for most stations. Rainfall non-stationarity started to occur in the middle of the 1960s for the annual, autumn, spring, and summer rainfalls and in the early 1970s for the winter rainfall in most of the stations. In addition, the average magnitude trend per decade is approximately −1.9%, −3.2%, +0.7%, +0.2%, and +2.4% for annual, winter, autumn, spring, and summer rainfalls, respectively. The multiple regression model can explain 62.2% of the spatial variability in annual rainfall, 58.9% of variability in winter, 75.9% of variability in autumn, 55.1% of variability in spring, and 32.2% of variability in summer. Moreover, rainfall spatial distribution maps were produced using the ordinary kriging method, through GIS software, representing the major rainfall range within the mountainous catchment of the study area. View Full-Text
Keywords: rainfall; trend analysis; Mann–Kendall; kriging interpolation rainfall; trend analysis; Mann–Kendall; kriging interpolation

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Stefanidis, S.; Stathis, D. Spatial and Temporal Rainfall Variability over the Mountainous Central Pindus (Greece). Climate 2018, 6, 75.

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