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Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (4): 377-392    DOI: 10.1007/s40333-023-0097-1     CSTR: 32276.14.s40333-023-0097-1
Research article     
Future meteorological drought conditions in southwestern Iran based on the NEX-GDDP climate dataset
Sakine KOOHI, Hadi RAMEZANI ETEDALI()
Department of Water Engineering, Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazvin 96818, Iran
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Abstract  

Investigation of the climate change effects on drought is required to develop management strategies for minimizing adverse social and economic impacts. Therefore, studying the future meteorological drought conditions at a local scale is vital. In this study, we assessed the efficiency of seven downscaled Global Climate Models (GCMs) provided by the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), and investigated the impacts of climate change on future meteorological drought using Standard Precipitation Index (SPI) in the Karoun River Basin (KRB) of southwestern Iran under two Representative Concentration Pathway (RCP) emission scenarios, i.e., RCP4.5 and RCP8.5. The results demonstrated that SPI estimated based on the Meteorological Research Institute Coupled Global Climate Model version 3 (MRI-CGCM3) is consistent with the one estimated by synoptic stations during the historical period (1990-2005). The root mean square error (RMSE) value is less than 0.75 in 77% of the synoptic stations. GCMs have high uncertainty in most synoptic stations except those located in the plain. Using the average of a few GCMs to improve performance and reduce uncertainty is suggested by the results. The results revealed that with the areas affected by wetness decreasing in the KRB, drought frequency in the North KRB is likely to increase at the end of the 21st century under RCP4.5 and RCP8.5 scenarios. At the seasonal scale, the decreasing trend for SPI in spring, summer, and winter shows a drought tendency in this region. The climate-induced drought hazard can have vast consequences, especially in agriculture and rural livelihoods. Accordingly, an increasing trend in drought during the growing seasons under RCP scenarios is vital for water managers and farmers to adopt strategies to reduce the damages. The results of this study are of great value for formulating sustainable water resources management plans affected by climate change.

Key words： climate change      meteorological drought      Global Climate Models (GCMs)      Standard Precipitation Index (SPI)      Representative Concentration Pathway (RCP)      NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP)      southwestern Iran     
Received: 04 March 2022      Published: 30 April 2023
Corresponding Authors: *Hadi RAMEZANI ETEDALI (E-mail: ramezani@eng.ikiu.ac.ir)
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Sakine KOOHI, Hadi RAMEZANI ETEDALI. Future meteorological drought conditions in southwestern Iran based on the NEX-GDDP climate dataset. Journal of Arid Land, 2023, 15(4): 377-392.

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http://jal.xjegi.com/10.1007/s40333-023-0097-1     OR     http://jal.xjegi.com/Y2023/V15/I4/377

Fig. 1 Location and spatial distribution of synoptic stations in the Karoun River Basin (KRB)
Synoptic station Elevation (m) Average annual precipitation (mm) Annual average temperature (°C) Climate type
Abadan 6.6 174.3 25.9 Extra-arid
Ahvaz 22.5 238.0 26.0 Arid
Aligudarz 2022.1 394.8 13.2 Mediterranean
Borujen 2260.0 255.2 12.8 Semi-arid
Borujerd 1629.0 473.5 14.9 Mediterranean
Bostan 7.8 211.3 24.1 Arid
Khorramabad 1147.8 487.9 16.7 Semi-arid
Kuhrang 2365.0 1406.8 9.9 Humid
Masjedsoleyman 320.5 457.2 25.0 Semi-arid
Ramhormoz 150.5 330.1 27.5 Arid
Safiabad 82.9 349.5 24.6 Semi-arid
Shahrekord 2048.9 327.3 11.9 Semi-arid
Yasuj 1816.3 886.0 15.0 Humid
Table 1 Brief description of 13 synoptic stations in the Karoun River Basin (KRB)
NEX-GDDP
resolution		 Original
resolution		 Center GCM
0.25°×0.25° 2.80°×2.80° Canadian Centre for Climate Modelling and Analysis in Canada CanESM2
0.25°×0.25° 0.94°×1.25° National Center for Atmospheric Research in the United States CCSM4
0.25°×0.25° 1.40°×1.40° National Centre for Meteorological Research in France CNRM-CM5
0.25°×0.25° 2.00°×2.50° National Oceanic and Atmospheric Administration (NOAA),
geophysical fluid dynamics laboratory in the United States		 GFDL-CM3
0.25°×0.25° 1.25°×2.50° Institute Pierre-Simon Laplace in France IPSL-CM5A-MR
0.25°×0.25° 1.40°×1.40° Meteorological Research Institute in Japan MRI-CGCM3
0.25°×0.25° 1.40°×1.40° Atmosphere and Ocean Research Institute in the University of Tokyo, National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology in Japan MIROC5
Table 2 Details and characteristics of Global Climate Models (GCMs) used in this study
Classification SPI Classification SPI
Near normal -0.50≤SPI≤0.00 Near normal 0.00≤SPI≤0.50
Abnormally dry -0.70≤SPI< -0.50 Abnormally wet 0.50<SPI≤0.70
Moderately dry -1.20≤SPI< -0.70 Moderately wet 0.70<SPI≤1.20
Severely dry -1.50≤SPI< -1.20 Severely wet 1.20<SPI≤1.50
Extremely dry -2.00≤SPI< -1.50 Extremely wet 1.50<SPI≤2.00
Exceptionally dry SPI< -2.00 Exceptionally wet SPI>2.00
Table 3 Classification of dry and wet conditions based on Standard Precipitation Index (SPI)
Fig. 2 Spatial distribution of root mean square error (RMSE) and correlation coefficient (CC) of SPI calculated based on Global Climate Models (GCMs) and synoptic stations as well as spatial distribution of standard deviation (SD) of SPI calculated from each Global Climate Model (GCM). (a1-a3), Canadian Earth System Model version 2 (CanESM2); (b1-b3), Community Climate System Model version 4 (CCSM4); (c1-c3), Center National for Research Meteorological Climate Model version 5 (CNRM-CM5); (d1-d3), Geophysical Fluid Dynamics Laboratory Coupled Model version 3 (GFDL-CM3); (e1-e3), Institute Pierre Simon Laplace Climate Model phase 5 Atmospheric Mid Resolution (IPSL-CM5A-MR); (f1-f3), Model for Interdisciplinary Research on Climate version 5 (MIROC5); (g1-g3), Meteorological Research Institute Coupled Global Climate Model version 3 (MRI-CGCM3).
Fig. 3 Spatial distribution of RMSE (a), SD (b), and CC (c) of SPI calculated from the average of all GCMs and observations
Fig. 4 Matrix plot of coefficient of variation (CV) between GCMs and synoptic stations. Class 1 (-4.0-700.0 m), Class 2 (700.0-1660.0 m), Class 3 (1660.0-2430.0 m), and Class 4 (2430.0-4375.0 m) are four distinct elevation classes that are divided based on the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM).
Synoptic station GCM
Borujerd Borujen Aligudarz Ahvaz Abadan
0.00-0.24 0.04-0.26 -0.01-0.23 0.23-0.41 0.24-0.42 CanESM2
0.07-0.29 0.12-0.32 0.06-0.28 0.36-0.52 0.31-0.48 CCSM4
0.04-0.26 0.08-0.29 0.07-0.29 0.26-0.44 0.27-0.44 CNRM-CM5
0.06-0.28 0.12-0.33 0.04-0.27 0.24-0.42 0.30-0.47 GFDL-CM3
0.02-0.25 0.09-0.30 0.03-0.25 0.32-0.48 0.48-0.62 IPSL-CM5A-MR
0.03-0.26 0.06-0.28 0.03-0.25 0.25-0.43 0.27-0.45 MRI-CGCM3
0.05-0.27 0.08-0.29 0.07-0.28 0.22-0.40 0.28-0.45 MIROC5
Ramhormoz Masjedsoleyman Kuhrang Khorramabad Bostan GCM
0.19-0.38 0.20-0.38 0.05-0.27 0.01-0.25 0.21-0.40 CanESM2
0.25-0.43 0.21-0.40 0.10-0.31 0.10-0.31 0.28-0.45 CCSM4
0.22-0.41 0.22-0.41 0.08-0.30 0.05-0.27 0.22-0.40 CNRM-CM5
0.18-0.37 0.17-0.37 0.07-0.29 0.05-0.28 0.23-0.41 GFDL-CM3
0.31-0.48 0.19-0.38 0.06-0.28 0.02-0.25 0.32-0.48 IPSL-CM5A-MR
0.20-0.39 0.21-0.39 0.09-0.30 0.04-0.26 0.23-0.41 MRI-CGCM3
0.20-0.39 0.22-0.40 0.07-0.29 0.05-0.27 0.25-0.43 MIROC5
Yasuj Shahrekord Safiabad GCM
0.05-0.27 0.06-0.28 0.14-0.34 CanESM2
0.11-0.32 0.10-0.31 0.21-0.39 CCSM4
0.07-0.29 0.10-0.31 0.16-0.35 CNRM-CM5
0.13-0.33 0.08-0.29 0.16-0.36 GFDL-CM3
0.11-0.32 0.07-0.28 0.18-0.37 IPSL-CM5A-MR
0.07-0.28 0.08-0.29 0.15-0.35 MRI-CGCM3
0.10-0.31 0.06-0.28 0.18-0.37 MIROC5
Table 4 Results of 95% uncertainty bounds of each GCM for 13 synoptic stations during historical period
Fig. 5 Time series of SPI in each elevation class during 1990-2005. (a), Class 1 (Ramhormoz station); (b), Class 2 (Khorramabad station); (c), Class 3 (Borujen station); (d), Class 4 (Kuhrang station).
Fig. 6 Spatial distribution of drought frequency during 1990-2005 (a), 2009-2039 (b and e), 2040-2069 (c and f), and 2070-2099 (d and g) under RCP4.5 and RCP8.5 scenarios
Fig. 7 Spatial distribution of wetness frequency during 1990-2005 (a), 2009-2039 (b and e), 2040-2069 (c and f), and 2070-2099 (d and g) under RCP4.5 and RCP8.5 scenarios
Fig. 8 Seasonal variations of SPI under RCP4.5 and RCP8.5 scenarios in Kuhrang (a1 and a2), Borujen (b1 and b2), Yasuj (c1 and c2), Aligudarz (d1 and d2), Borujerd (e1 and e2), and Khorramabad (f1 and f2) stations. Bars represent standard errors.
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