Nivar

Nivar

Drought monitoring based on satellite index (SDI) and TRMM data. (Case Study: Khorasan Razavi province)

Document Type : Original Article

Authors
Mozhdeh Salimi fard 1
Seyed Hossein Sanaei-nejad 2
adel sepehr 3
leila sabet dizavandi 4
1 Ph-D student of agrometeorology / Ferdowsi university
2 Associate Professor of water engineering department , Ferdowsi university of mashhad.
3 Assistant Professor, Faculty of Natural Resources and the Environment, Ferdowsi university of Mashhad
4 meteorology
10.30467/nivar.2018.125918.1085
Abstract
Iran located in arid and semi-arid area has many parts with high susceptibility to drought. The average rainfall of Iran is less than a third of the average annual rainfall in the world which has an inappropriate spatial -temporal distribution. Meteorological indices can be used for drought monitoring calculated by situations data. One of the main problems for applying these indices in Iran is inappropriate distribution of station and lack of data. In other hand, remote sensing technology is able to extract data from remote area. In this study in order to analyze drought risk in Khorasan Razavi province Synthesis Drought Index (SDI),”between 2001 to 2010” was applied. Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Precipitation Condition Index (PCI) through Principal Component Analysis (PCA) were measured for estimating SDI. For assessing the accuracy of SDI, correlation between these indicators and SPI (3, 6, 9-month) have been studied during the growing season, comparison was also done between total annual rainfall and long-term average rainfall in 10 synoptic stations in the studied area, as well as correlation between two indices VCI and SDI with the yield of rainfed wheat and barley. The results indicated that drought has been occurred in years 2001, 2002, 2006 and 2008 in the Khorasn Razavi province. The result of validation showed high correlation between SDI and SPI. Also the results showed that the Synthesis Drought Index in addition to monitor meteorological drought with ability of enhanced spatial resolution could be applied for identifying agricultural drought.
Keywords
Drought
NDVI data
Land surface temperature
TRMM sensors
Synthesized Drought Index (SDI)
Subjects
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Nivar
Volume 42, 102-103
September 2018
Pages 19-30

  • Receive Date 03 April 2018
  • Revise Date 14 August 2018
  • Accept Date 26 November 2018
  • Publish Date 23 September 2018