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Journal of Arid Land  2020, Vol. 12 Issue (2): 318-330    DOI: 10.1007/s40333-020-0095-5
Prediction of meteorological drought in arid and semi-arid regions using PDSI and SDSM: a case study in Fars Province, Iran
Sheida DEHGHAN1, Nasrin SALEHNIA2, Nasrin SAYARI1,*(), Bahram BAKHTIARI1
1 Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman 7616914111, Iran
2 Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 9177949207, Iran
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Drought is one of the most significant environmental disasters, especially in arid and semi-arid regions. Drought indices as a tool for management practices seeking to deal with the drought phenomenon are widely used around the world. One of these indicators is the Palmer drought severity index (PDSI), which is used in many parts of the world to assess the drought situation and continuation. In this study, the drought state of Fars Province in Iran was evaluated by using the PDSI over 1995-2014 according to meteorological data from six weather stations in the province. A statistical downscaling model (SDSM) was used to apply the output results of the general circulation model in Fars Province. To implement data processing and prediction of climate data, a statistical period 1995-2014 was considered as the monitoring period, and a statistical period 2019-2048 was for the prediction period. The results revealed that there is a good agreement between the simulated precipitation (R2>0.63; R2, determination coefficient; MAE<0.52; MAE, mean absolute error; RMSE<0.56; RMSE, Root Mean Squared Error) and temperature (R2>0.95, MAE<1.74, and RMSE<1.78) with the observed data from the stations. The results of the drought monitoring model presented that dry periods would increase over the next three decades as compared to the historical data. The studies showed the highest drought in the meteorological stations Abadeh and Lar during the prediction period under two future scenarios representative concentration pathways (RCP4.5 and RCP8.5). According to the results of the validation periods and efficiency criteria, we suggest that the SDSM is a proper tool for predicting drought in arid and semi-arid regions.

Key wordsPDSI      SDSM      RCP4.5      RCP8.5      climate change      extreme drought     
Received: 14 February 2019      Published: 30 April 2020
Corresponding Authors: SAYARI Nasrin     E-mail:
Cite this article:

Sheida DEHGHAN, Nasrin SALEHNIA, Nasrin SAYARI, Bahram BAKHTIARI. Prediction of meteorological drought in arid and semi-arid regions using PDSI and SDSM: a case study in Fars Province, Iran. Journal of Arid Land, 2020, 12(2): 318-330.

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Fig. 1 Location of the study area, Fars Province of Iran (a). The north and northwest part has cold winter and mild summer; the central area has rainy, mild winter and hot, dry summer; and the south and southeast part has cold winter and hot summer (b). N, north; NW, northwest; S, south, SE, southeast.
Meteorological station Location Elevation (m) Mean annual temperature (°C) Total daily precipitation (mm) Climate
Shiraz 29°32′N, 52°36′E 1484 18.6 6370.1 Semi-arid
Fasa 28°58′N, 53°41′E 1288 19.4 5429.3 Semi-arid
Abadeh 31°11′N, 52°40′E 2030 14.4 2649.4 Arid
Darab 28°47′N, 54°17′E 1098 22.1 4983.8 Arid
Lar 27°42′N, 54°17′E 792 23.9 3533.2 Arid
Eghlid 30°54′N, 52°38′E 2300 13.0 6325.9 Arid
Table 1 Characteristics of the six meteorological stations in Fars Province of Iran during 1995-2014
PDSI value Classification
≥4.00 Extreme wet
3.00-3.99 Very wet
2.00-2.99 Moderate wet
1.00-1.99 Slight wet
0.50-0.99 Incipient wet spell
0.49- -0.49 Near normal
-0.50- -0.99 Incipient dry spell
-1.00- -1.99 Mild drought
-2.00- -2.99 Moderate drought
-3.00- -3.99 Severe drought
≤-4.00 Extreme drought
Table 2 Drought classification by PDSI value
Fig. 2 Flowchart of estimating climate variables through the statistical downscaling model (SDSM). NCEP, national center of environmental prediction; GCM, global climate model.
Meteorological station Precipitation Temperature
Shiraz 0.89 0.39 0.19 0.80 0.99 0.44 0.37 0.99
Fasa 0.63 0.50 0.36 0.65 0.99 1.14 0.98 0.99
Abadeh 0.66 0.21 0.15 0.61 0.97 1.42 0.70 0.97
Darab 0.71 0.36 0.23 0.75 0.95 1.78 1.74 0.95
Lar 0.75 0.56 0.52 0.64 0.99 0.52 0.45 0.99
Eghlid 0.93 0.13 0.14 0.97 0.96 1.42 1.14 0.96
Table 3 Results of the model evaluation in the validation period 2019-2033
Fig. 3 Observed (1995-2014) and simulated (2019-2048) maximum monthly precipitation (a-f) and monthly average temperature (g-l) under RCP4.5 and RCP8.5 scenarios at selected stations of Fars Province, Iran
Meteorological station Year Annual precipitation (mm) Precipitation of the previous year (mm)
Abadeh 2008 36.3 152.7
Darab 2001 100.0 195.4
Eghlid 2000 232.0 299.0
2008 123.1 386.2
Fasa 2001 138.2 243.7
2008 112.5 185.3
Lar 2000 102.1 123.6
Shiraz 2008 125.8 241.7
2010 94.3 281.0
Table 4 Extreme drought events in Fars Province during 1995-2014
Meteorological station RCP4.5 RCP8.5
2039-2048 2029-2038 2019-2028 2039-2048 2029-2038 2019-2028
Shiraz 75 66 68 46 79 77
Fasa 59 59 64 58 88 66
Abadeh 85 80 82 68 58 101
Darab 58 62 66 63 57 65
Lar 64 75 64 69 88 72
Eghlid 33 67 54 64 65 57
Table 5 Number of dry months in the prediction period 2019-2048
Fig. 4 Boxplot for the monthly Palmer drought severity index (PDSI) at the Abadeh station under RCP4.5 for the prediction period 2019-2048. The horizontal line inward the box shows the group median (black line), and the multiple sign refers to the mean. The circles refer to outlier data.
Fig. 5 Boxplot for the monthly Palmer drought severity index (PDSI) at the Abadeh station under RCP8.5 for the prediction period 2019-2048. The horizontal line inward the box shows the group median (black line), and the multiply sign refers to the mean. The circles refer to outlier data.
Fig. 6 PDSI values under RCP4.5 during 2019-2048
Fig. 7 PDSI values under RCP8.5 during 2019-2048
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