Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 1141–1145, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-1141-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 1141–1145, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-1141-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Oct 2019

19 Oct 2019

EVALUATION OF LONG TERM TREND OF DIFFERENT DROUGHT INDICES USING MANN-KENDALL AND SEN’S SLOPE ESTIMATOR OVER IRAN

S. Zakeri1, A. Samkhaniani2, S. Adeli1, and Z. Nikraftar1 S. Zakeri et al.
  • 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
  • 2Babol Noshirvani University of Technology, Civil Engineering Department, P.O. Box484, Shariati Ave., Babol, Mazandaran 47148-71167, Iran

Keywords: Mann-Kendall, Sen’s slope estimator, drought, Standardized Precipitation Index, Standardized Soil moisture Index, Total Storage Deficit Index

Abstract. Historically droughts have had a huge impact on conditions of life on earth. With growing population of the Earth the impacts of droughts grow even more severe. Human beings are also affected by drought like other life forms. Studies have taken place to understand and thereby mitigate the effects of these climatological phenomena on humans. The study of drought trends is of great importance since it is directly related to food and water resources availability, that could become scarce in case of droughts. In this paper by using SPI, SSI and TSDI indices we try to understand the drought behaviour in Iran using Mann-Kendall and Sen’s slope methods. Canonical correlation analysis between SPI and SSI shows a positive correlation for the most parts of Iran. Also the correlation between TSDI and SPI/SSI is positive for majority of the Iran. Drought trends in Iran are analysed using Mann-Kendall and Sen’s slope estimator with 95% confidence level.