Spatial and Time Variability of Drought Based on SPI and RDI with Various Time Scales

Published on Feb 1, 2018in Water Resources Management2.99
· DOI :10.1007/s11269-017-1856-6
Abdelaaziz Merabti2
Estimated H-index: 2
('ENS Paris': École Normale Supérieure),
Diogo S. Martins6
Estimated H-index: 6
(University of Lisbon)
+ 1 AuthorsLuis S. Pereira51
Estimated H-index: 51
(University of Lisbon)
The spatial and temporal variability of droughts were studied for the Northeast Algeria using SPI and RDI computed with monthly precipitation data from 123 rainfall stations and CFSR reanalysis monthly temperature data covering the period 1979–80 to 2013–14. The gridded temperature data was interpolated to all the locations having precipitation data, thus providing to compute SPI and RDI with the time scales of 3-, 6- and 12-month with the same observed rainfall data. Spatial and temporal patterns of droughts were obtained using Principal Component Analysis in S-Mode with Varimax rotation applied to both SPI and RDI. For all time scales of both indices, two principal components were retained identifying two sub-regions that are similar and coherent for all SPI and RDI time scales. Both components explained more than 70% and 74% of drought spatial variability of SPI and RDI, respectively. The identified sub-regions are similar and coherent for all SPI and RDI time scales. The Modified Mann-Kendall test was used to assess trends of the RPC scores, which have shown non-significant trends for decreasing drought occurrence and severity in both identified drought sub-regions and all time scales. Both indices have shown a coherent and similar behavior, however with RDI likely showing to identify more severe and moderate droughts in the southern and more arid sub-region which may be due to its ability to consider influences of global warming. Results for RDI are quite uniform relative to time scales and show smaller differences among the various climates when compared with SPI. Further assessments covering the NW and NE of Algeria using longer time series should be performed to better understand the behavior of both indices.
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  • Citations (3)
#1Abdelaaziz Merabti ('ENS Paris': École Normale Supérieure)H-Index: 2
#2Hind Meddi ('ENS Paris': École Normale Supérieure)H-Index: 10
Last.Luis S. Pereira (Instituto Superior de Agronomia)H-Index: 51
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#1Muhammad Imran Khan (NEAU: Northeast Agricultural University)H-Index: 4
#2Dong Liu (NEAU: Northeast Agricultural University)H-Index: 7
Last.Chen Cheng (NEAU: Northeast Agricultural University)H-Index: 1
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#1George Tsakiris (NTUA: National Technical University of Athens)H-Index: 20
#2Nikos Kordalis (NTUA: National Technical University of Athens)H-Index: 1
Last.Harris Vangelis (NTUA: National Technical University of Athens)H-Index: 10
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#1Dimitris Tigkas (NTUA: National Technical University of Athens)H-Index: 9
#2Harris Vangelis (NTUA: National Technical University of Athens)H-Index: 10
Last.George Tsakiris (NTUA: National Technical University of Athens)H-Index: 20
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#1Kai Xu (THU: Tsinghua University)H-Index: 3
#2Dawen Yang (THU: Tsinghua University)H-Index: 4
Last.Yan Shen (CMA: China Meteorological Administration)H-Index: 1
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