Theoretical and Applied Climatology

, Volume 130, Issue 1–2, pp 249–260 | Cite as

Trends of precipitation extreme indices over a subtropical semi-arid area using PERSIANN-CDR

  • Pari-Sima Katiraie-BoroujerdyEmail author
  • Hamed Ashouri
  • Kuo-lin Hsu
  • Soroosh Sorooshian
Original Paper


In this study, satellite-based daily precipitation estimation data from precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)-climate data record (CDR) are being evaluated in Iran. This dataset (0.25°, daily), which covers over three decades of continuous observation beginning in 1983, is evaluated using rain-gauge data for the period of 1998–2007. In addition to categorical statistics and mean annual amount and number of rainy days, ten standard extreme indices were calculated to observe the behavior of daily extremes. The results show that PERSIANN-CDR exhibits reasonable performance associated with the probability of detection and false-alarm ratio, but it overestimates precipitation in the area. Although PERSIANN-CDR mostly underestimates extreme indices, it shows relatively high correlations (between 0.6316–0.7797) for intensity indices. PERSIANN-CDR data are also used to calculate the trend in annual amounts of precipitation, the number of rainy days, and precipitation extremes over Iran covering the period of 1983–2012. Our analysis shows that, although annual precipitation decreased in the western and eastern regions of Iran, the annual number of rainy days increased in the northern and northwestern areas. Statistically significant negative trends are identified in the 90th percentile daily precipitation, as well as the mean daily precipitation from wet days in the northern part of the study area. The positive trends of the maximum annual number of consecutive dry days in the eastern regions indicate that the dry periods became longer in these arid areas.


Daily Precipitation Extreme Index Zagros Mountain Satellite Product Geostationary Earth Orbit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  • Pari-Sima Katiraie-Boroujerdy
    • 1
    Email author
  • Hamed Ashouri
    • 2
  • Kuo-lin Hsu
    • 2
  • Soroosh Sorooshian
    • 2
  1. 1.Faculty of Marine Science and TechnologyTehran North Branch, Islamic Azad UniversityTehranIran
  2. 2.Department of Civil and Environmental EngineeringCenter for Hydrometeorology and Remote Sensing (CHRS), The Henry Samueli School of Engineering, University of CaliforniaIrvineUSA

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