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B ulgarian Academy of Sciences N ATIONAL INSTITUTE OF METEOROLOGY AND HYDROLOGY

B ulgarian Academy of Sciences N ATIONAL INSTITUTE OF METEOROLOGY AND HYDROLOGY. A Case Study on Utilization of Precipitation Indices in Bulgaria V esselin Alexandrov B . Dubuisson *, J.M. Moisselin *, K.Koleva Ohrid, 2006. *. OBJECTIVES.  to explore various (> 40) STARDEX

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B ulgarian Academy of Sciences N ATIONAL INSTITUTE OF METEOROLOGY AND HYDROLOGY

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  1. Bulgarian Academy of Sciences NATIONAL INSTITUTE OF METEOROLOGY AND HYDROLOGY A Case Study on Utilization of Precipitation Indices in Bulgaria Vesselin Alexandrov B.Dubuisson*,J.M.Moisselin*, K.Koleva Ohrid, 2006 *

  2. OBJECTIVES to explore various (> 40) STARDEX precipitation indices Core precipitation indices: prec90 - 90th percentile of rainday amounts R5d - greatest 5-day total rainfall SDII - simple daily intensity CDD - max number of consecutive dry days R90T - % of total rainfall from events > long- term 90th percentile R90N - number of events > long-term 90th percentile

  3. Weather stations in Bulgaria with long-term records, used in the study

  4. HOMOGENIZATION METHOD The currently used in Météo-France homogenization procedure, which does not require computation of regional reference series, was applied  The Caussinus-Mestre method, with a double-step procedure was used to detect multiple breaks and outliers in the previously controlled average precipitation long-term series  A two factor linear model was applied for break correction in the precipitationseries

  5. Anomalies of annual precipitation in the weather stations, used in the study, during application of data quality control

  6. - missing - break - outlier Homogenization of monthly precipitation data in weather station Boshurishte – first detection of breaks (triangles) and outliers (A)

  7. validated breaks and then corrected Homogenization of monthly precipitation data in weather station Boshurishte – break correction

  8. annual variability: 90th percentile of rainday amounts (1951-2000)

  9. Annual prec90 trends (1951-2000) by applying the Spearman test; dark red arrows – significant increasing trend; red arrows – increasing trend; blue arrows – decreasing trend; dark blue arrows – significant decreasing trend

  10. Annual R5d trendstrends (1951-2000) by applying the Spearman test; dark red arrows – significant increasing trend; red arrows – increasing trend; blue arrows – decreasing trend; dark blue arrows – significant decreasing trend

  11. Annual SDIItrends (1951-2000) by applying the Spearman test; dark red arrows – significant increasing trend; red arrows – increasing trend; blue arrows – decreasing trend; dark blue arrows – significant decreasing trend

  12. Annual CDDtrends (1951-2000) by applying the Spearman test; dark red arrows – significant increasing trend; red arrows – increasing trend; blue arrows – decreasing trend; dark blue arrows – significant decreasing trend

  13. Annual R90Ttrends (1951-2000) by applying the Spearman test; dark red arrows – significant increasing trend; red arrows – increasing trend; blue arrows – decreasing trend; dark blue arrows – significant decreasing trend

  14. Annual R90Ntrends (1951-2000) by applying the Spearman test; dark red arrows – significant increasing trend; red arrows – increasing trend; blue arrows – decreasing trend; dark blue arrows – significant decreasing trend

  15. CONCLUSIONS The trends (1951-2000) in the core STARDEX precipitation indices, are weak and are not significant in general. The significant trends were observed in separate weather stations or areas only. The trends in most cases are with low spatial coherence. Additional work is planned to be done. Further analysis of precipitation related indices is needed as well as comparison of the results with the ones obtained in different European regions.

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