Please wait a minute...
Journal of Arid Land  2022, Vol. 14 Issue (11): 1212-1233    DOI: 10.1007/s40333-022-0106-9     CSTR: 32276.14.s40333-022-0106-9
Research article     
Meteorological drought in semi-arid regions: A case study of Iran
Hushiar HAMARASH1,*(), Rahel HAMAD1, Azad RASUL2
1Scientific Research Center, Soran University, Soran 44008, Iraq
2Faculty of Arts, Department of Geography, Soran University, Soran 44008, Iraq
Download: HTML     PDF(2530KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

Drought occurs in almost all climate zones and is characterized by prolonged water deficiency due to unbalanced demand and supply of water, persistent insufficient precipitation, lack of moisture, and high evapotranspiration. Drought caused by insufficient precipitation is a temporary and recurring meteorological event. Precipitation in semi-arid regions is different from that in other regions, ranging from 50 to 750 mm. In general, the semi-arid regions in the west and north of Iran received more precipitation than those in the east and south. The Terrestrial Climate (TerraClimate) data, including monthly precipitation, minimum temperature, maximum temperature, potential evapotranspiration, and the Palmer Drought Severity Index (PDSI) developed by the University of Idaho, were used in this study. The PDSI data was directly obtained from the Google Earth Engine platform. The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) on two different scales were calculated in time series and also both SPI and SPEI were shown in spatial distribution maps. The result showed that normal conditions were a common occurrence in the semi-arid regions of Iran over the majority of years from 2000 to 2020, according to a spatiotemporal study of the SPI at 3-month and 12-month time scales as well as the SPEI at 3-month and 12-month time scales. Moreover, the PDSI detected extreme dry years during 2000-2003 and in 2007, 2014, and 2018. In many semi-arid regions of Iran, the SPI at 3-month time scale is higher than the SPEI at 3-month time scale in 2000, 2008, 2014, 2015, and 2018. In general, this study concluded that the semi-arid regions underwent normal weather conditions from 2000 to 2020. In a way, moderate, severe, and extreme dry occurred with a lesser percentage, gradually decreasing. According to the PDSI, during 2000-2003 and 2007-2014, extreme dry struck practically all hot semi-arid regions of Iran. Several parts of the cold semi-arid regions, on the other hand, only experienced moderate to severe dry from 2000 to 2003, except for the eastern areas and wetter regions. The significance of this study is the determination of the spatiotemporal distribution of meteorological drought in semi-arid regions of Iran using strongly validated data from TerraClimate.



Key wordsmeteorological drought      precipitation      Standardized Precipitation Index      Standardized Precipitation Evapotranspiration Index      Palmer Drought Severity Index      Iran     
Received: 26 June 2022      Published: 30 November 2022
Corresponding Authors: *Hushiar HAMARASH (E-mail: hrh670h@src.soran.edu.iq)
Cite this article:

Hushiar HAMARASH, Rahel HAMAD, Azad RASUL. Meteorological drought in semi-arid regions: A case study of Iran. Journal of Arid Land, 2022, 14(11): 1212-1233.

URL:

http://jal.xjegi.com/10.1007/s40333-022-0106-9     OR     http://jal.xjegi.com/Y2022/V14/I11/1212

Fig. 1 Spatiotemporal distribution of precipitation in semi-arid regions of Iran in 2000 (a), 2003 (b), 2005 (c), 2007 (d), 2009 (e), 2012 (f), 2014 (g), 2016 (h), 2017 (i), 2018 (j), 2019 (k), and 2020 (l)
Variable Dataset Spatial resolution Temporal resolution
Maximum temperature (°C) WorldClim V1.4 and CRU TS4.0 1/24° and 0.50° Monthly
Minimum temperature (°C) WorldClim V1.4 and CRU TS4.0 1/24° and 0.50° Monthly
Precipitation accumulation (mm) WorldClim V2.0, CRU TS4.0, and JRA-55 1/24°, 0.50°, and 1.25° Monthly
Wind speed at 10 m (m/s) WorldClim V2.0 and JRA-55 1/24° Monthly
Vapor pressure at 2 m (kPa) WorldClim V2.0, CRU TS4.0, and JRA-55 1/24°, 0.50°, and 1.25° Monthly
Vapor pressure deficit (kPa) Root zone storage capacity 4638.3 m Time invariant
Snow water equivalent (mm) - 4638.3 m Time invariant
Downward shortwave radiation flux at the surface (W/m2) WorldClim V2.0 and JRA-55 1/24° and 1.25° Monthly
Soil moisture (mm) Root zone storage capacity 4638.3 m Time invariant
Runoff (mm) - 4638.3 m Time invariant
Reference evapotranspiration (mm) - 4638.3 m Time invariant
Climate water deficit (mm) - 4638.3 m Time invariant
Palmer Drought Severity Index - 4638.3 m Time invariant
Actual evapotranspiration (mm) - 4638.3 m Time invariant
Table 1 Characteristics of Terrestrial Climate (TerraClimate)
Range Class
≥2.00 Extreme wet
1.50-1.99 Severe wet
1.00-1.49 Moderate wet
-0.99-0.99 Near normal
-1.00- -1.49 Moderate dry
-1.50- -1.99 Severe dry
≤ -2.00 Extreme dry
Table 2 Range of the Standard Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) for drought
Fig. 2 Average Palmer Drought Severity Index (PDSI) values in cold semi-arid regions of Iran
Fig. 3 Average PDSI values in hot semi-arid regions of Iran
Fig. 4 Standard Precipitation Index (SPI) at 3-month time scale (SPI-3) and Standardized Precipitation Evapotranspiration Index (SPEI) at 3-month time scale (SPEI-3) in several semi-arid regions of Iran during 2000-2020. (a), East Azerbaijan Province; (b), Bushehr Province; (c), Fars Province; (d), Hormozgan Province; (e), Ilam Province; (f), Kerman Province; (g), Kermanshah Province; (h), Markazi Province; (i), North Khorasan Province; (j), Qom Province; (k), Razavi Khorasan Province; (l), Mazandaran Province; (m), Semnan Province; (n), South Khorasan Province; (o), West Azerbaijan Province.
Fig. S1 Standard Precipitation Index (SPI) at 3-month time scale (SPI-3) and Standardized Precipitation Evapotranspiration Index (SPEI) at 3-month time scale (SPEI-3) in several semi-arid regions of Iran during 2000-2020. (a), Alborz Province; (b), Sistan and Baluchestan Province; (c), Golestan Province; (d), Hamadan Province; (e), Kurdistan Province; (f), Esfahan Province; (g), Kohgiluyeh and Boyer-Ahmad Province; (h), Lorestan Province; (i), Tehran Province; (j), Yazd Province.
Fig. 5 SPI at 12-month time scale (SPI-12) and SPEI at 12-month time scale (SPEI-12) in several semi-arid regions of Iran during 2000-2020. (a), East Azerbaijan Province; (b), Bushehr Province; (c), Fars Province; (d), Hormozgan Province; (e), Ilam Province; (f), Kerman Province; (g), Kermanshah Province; (h), Markazi Province; (i), North Khorasan Province; (j), Qom Province; (k), Razavi Khorasan Province; (l), Mazandaran Province; (m), Semnan Province; (n), South Khorasan Province; (o), West Azerbaijan Province.
Fig. S2 SPI at 12-month time scale (SPI-12) and SPEI at 12-month time scale (SPEI-12) in several semi-arid regions of Iran during 2000-2020. (a), Alborz Province; (b), Sistan and Baluchestan Province; (c), Golestan Province; (d), Hamadan Province; (e), Kurdistan Province; (f), Esfahan Province; (g), Kohgiluyeh and Boyer-Ahmad Province; (h), Lorestan Province; (i), Tehran Province; (j), Yazd Province.
Fig. 6 Number of dry and wet months in semi-arid regions of Iran based on SPI-3 (a), SPEI-3 (b), SPI-12 (c), and SPEI-12 (d) during 2000-2020
Fig. S3 Spatial and temporal distribution of SPI-12 in winter and summer in semi-arid regions of Iran. (a), Winter 2002; (b), Summer 2002; (c), Winter 2005; (d), Summer 2005; (e), Winter 2007; (f), Summer 2007; (g), Winter 2010; (h), Summer 2010; (i), Winter 2012; (j), Summer 2012; (k), Winter 2013; (l), Summer 2013; (m), Winter 2015; (n), Summer 2015; (o), Winter 2017; (p), Summer 2017; (q), Winter 2019; (r), Summer 2019; (s), Winter 2020; (t), Summer 2020.
Fig. S4 Spatial and temporal distribution of SPEI-12 in winter and summer in semi-arid regions of Iran. (a), Winter 2002; (b), Summer 2002; (c), Winter 2005; (d), Summer 2005; (e), Winter 2007; (f), Summer 2007; (g), Winter 2010; (h), Summer 2010; (i), Winter 2012; (j), Summer 2012; (k), Winter 2013; (l), Summer 2013; (m), Winter 2015; (n), Summer 2015; (o), Winter 2017; (p), Summer 2017; (q), Winter 2019; (r), Summer 2019; (s), Winter 2020; (t), Summer 2020.
[1]   Abatzoglou J T, Dobrowski S Z, Parks S A, et al. 2018a. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. [2022-05-01]. https://www.climatologylab.org/TerraClimate.html.
[2]   Abatzoglou J T, Dobrowski S Z, Parks S A, et al. 2018b. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific Data, 5: 170191, doi: 10.1038/sdata.2017.191.
doi: 10.1038/sdata.2017.191
[3]   Abatzoglou J T, Dobrowski S Z, Parks S A, et al. 2021. TerraClimate Individual years for +2C and +4C climate futures. [2022-09-08]. https://samapriya.github.io/awesome-gee-community-datasets/projects/terraclim/.
[4]   Abdelmigid H M, Baz M, AlZain M A, et al. 2022. Spatiotemporal deep learning model for prediction of Taif Rose phenotyping. Agronomy, 12(4): 807, doi: 10.3390/agronomy12040807.
doi: 10.3390/agronomy12040807
[5]   Alijanian M, Rakhshandehroo G R, Mishra A, et al. 2019. Evaluation of remotely sensed precipitation estimates using PERSIANN-CDR and MSWEP for spatio-temporal drought assessment over Iran. Journal of Hydrology, 579: 124189, doi: 10.1016/j.jhydrol.2019.124189.
doi: 10.1016/j.jhydrol.2019.124189
[6]   Allen R G, Pereira L S, Raes D, et al. 1998. Crop evapotranspiration-guidelines for computing crop water requirements. In: FAO Irrigation & Drainage Paper No. 56. Rome, Italy.
[7]   Andreadis K M, Clark E A, Wood A W, et al. 2005. Twentieth-century drought in the conterminous United States. Journal of Hydrometeorology, 6(6): 985-1001.
doi: 10.1175/JHM450.1
[8]   Araneda R J, Puertas J, Maia R, et al. 2020. Unified framework for drought monitoring and assessment in a transboundaryriver basin. In: Uijttewaal W, Franca M, Valero D, et al. River Flow (1st ed.). London: CRC Press. 1081-1086.
[9]   Banimahd S A, Khalili D. 2013. Factors influencing Markov chains predictability characteristics, utilizing SPI, RDI, EDI and SPEI drought indices in different climatic zones. Water Resources Management, 27(11): 3911-3928.
doi: 10.1007/s11269-013-0387-z
[10]   Bari Abarghouei H, Asadi Zarch M A, Dastorani M T, et al. 2011. The survey of climatic drought trend in Iran. Stochastic Environmental Research and Risk Assessment, 25(6): 851-863.
doi: 10.1007/s00477-011-0491-7
[11]   Beyaztas U, Arikan B B, Beyaztas B H, et al. 2018. Construction of prediction intervals for Palmer Drought Severity Index using bootstrap. Journal of Hydrology, 559: 461-470.
doi: 10.1016/j.jhydrol.2018.02.021
[12]   Chen F W, Liu C W. 2012. Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy and Water Environment, 10(3): 209-222.
doi: 10.1007/s10333-012-0319-1
[13]   Darand M. 2015. Drought monitoring in Iran by palmer severity drought index (PDSI) and correlation with oceanic atmospheric teleconnection patterns. Geographical Research, 29(4): 67-82.
[14]   Dash B K, Rafiuddin M, Khanam F, et al. 2012. Characteristics of meteorological drought in Bangladesh. Natural Hazards, 64(2): 1461-1474.
doi: 10.1007/s11069-012-0307-1
[15]   Dehghan S, Salehnia N, Sayari N, et al. 2020. 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, 12(2): 318-330.
doi: 10.1007/s40333-020-0095-5
[16]   Guo H, Bao A M, Liu T, et al. 2017. Meteorological drought analysis in the Lower Mekong Basin using satellite-based long-term CHIRPS product. Sustainability, 9(6): 901, doi: 10.3390/su9060901.
doi: 10.3390/su9060901
[17]   Hamarash H R, Rasul A, Hamad R O, et al. 2022. A review of methods used to monitor and predict droughts. Preprints, 2022080539, doi: 10.20944/preprints202208.0539.v1.
doi: 10.20944/preprints202208.0539.v1
[18]   Hamed M M, Nashwan M S, Shahid S. 2021. Performance evaluation of reanalysis precipitation products in Egypt using fuzzy entropy time series similarity analysis. International Journal of Climatology, 41(11): 5431-5446.
doi: 10.1002/joc.7286
[19]   Hossein Z L, Reza F H, Fardin B. 2014. Evaluation of the wheat agricultural drought return period in the province of Fars using RDI index. Journal of Water Resources Engineering, Islamic Azad University, 7(22): 1-10.
[20]   Huerta A, Lavado W, Rau P. 2020. The vulnerability of water availability in Peru due to climate change: A probabilistic Budyko analysis. [2022-09-08]. https://ui.adsabs.harvard.edu/abs/2020EGUGA.22.3766H/abstract.
[21]   Karakani E G, Malekian A, Gholami S, et al. 2021. Spatiotemporal monitoring and change detection of vegetation cover for drought management in the Middle East. Theoretical and Applied Climatology, 144(1): 299-315.
doi: 10.1007/s00704-021-03543-x
[22]   Karl T R. 1983. Some spatial characteristics of drought duration in the United States. Journal of Applied Meteorology and Climatology, 22(8): 1356-1366.
[23]   Kazemzadeh M, Malekian A. 2016. Spatial characteristics and temporal trends of meteorological and hydrological droughts in northwestern Iran. Natural Hazards, 80(1): 191-210.
doi: 10.1007/s11069-015-1964-7
[24]   Kheyri R, Mojarrad F, Masompour J, et al. 2021. Evaluation of drought changes in Iran using SPEI and SC-PDSI. The Journal of Spatial Planning, 25(1): 175-206.
[25]   Kodandapani N, Parks S A. 2019. Effects of drought on wildfires in forest landscapes of the Western Ghats, India. International Journal of Wildland Fire, 28(6): 431-444.
doi: 10.1071/WF18188
[26]   Kousari M R, Dastorani M T, Niazi Y, et al. 2014. Trend detection of drought in arid and semi-arid regions of Iran based on implementation of reconnaissance drought index (RDI) and application of non-parametrical statistical method. Water Resources Management, 28(7): 1857-1872.
doi: 10.1007/s11269-014-0558-6
[27]   Liu C H, Yang C P, Yang Q, et al. 2021. Spatiotemporal drought analysis by the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) in Sichuan Province, China. Scientific Reports, 11: 1280, doi: 10.1038/s41598-020-80527-3.
doi: 10.1038/s41598-020-80527-3 pmid: 33446853
[28]   Martinez-Villalobos C, Neelin J D. 2019. Why do precipitation intensities tend to follow gamma distributions? Journal of the Atmospheric Sciences, 76(11): 3611-3631.
doi: 10.1175/JAS-D-18-0343.1
[29]   McKee T B, Doesken N J, Kleist J. 1993. The relationship of drought frequency and duration to time scales. In:Proceedings of the 8th Conference on Applied Climatology. Boston, USA.
[30]   Nasrollahi M, Khosravi H, Moghaddamnia A, et al. 2018. Assessment of drought risk index using drought hazard and vulnerability indices. Arabian Journal of Geosciences, 11: 606, doi: 10.1007/s12517-018-3971-y.
doi: 10.1007/s12517-018-3971-y
[31]   Nejadrekabi M, Eslamian S, Zareian M J. 2022. Spatial statistics techniques for SPEI and NDVI drought indices: A case study of Khuzestan Province. International Journal of Environmental Science and Technology, 19: 6573-6594.
doi: 10.1007/s13762-021-03852-8
[32]   Neto A K, Ribeiro R B, Pruski F F. 2022. Assessment water balance through different sources of precipitation and actual evapotranspiration. [2022-09-08]. https://doi.org/10.21203/rs.3.rs-1443692/v1.
doi: https://doi.org/10.21203/rs.3.rs-1443692/v1
[33]   Palmer W C. 1965. Meteorological Drought. Washington: Office of Climatology, US Weather Bureau, 7-12.
[34]   Peel M C, Finlayson B L, McMahon T A. 2007. Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences, 11(5): 1633-1644.
doi: 10.5194/hess-11-1633-2007
[35]   Pei Z F, Fang S B, Wang L, et al. 2020. Comparative analysis of drought indicated by the SPI and SPEI at various timescales in Inner Mongolia, China. Water, 12(7): 1925, doi: 10.3390/w12071925.
doi: 10.3390/w12071925
[36]   Rahimi J, Laux P, Khalili A. 2020. Assessment of climate change over Iran: CMIP 5 results and their presentation in terms of Köppen-Geiger climate zones. Theoretical and Applied Climatology, 141(1): 183-199.
doi: 10.1007/s00704-020-03190-8
[37]   Salvacion A R. 2022. Multiscale drought hazard assessment in the Philippines. Computers in Earth and Environmental Sciences, doi: 10.1016/B978-0-323-89861-4.00024-5.
doi: 10.1016/B978-0-323-89861-4.00024-5
[38]   Sharafati A, Nabaei S, Shahid S. 2020. Spatial assessment of meteorological drought features over different climate regions in Iran. International Journal of Climatology, 40(3): 1864-1884.
doi: 10.1002/joc.6307
[39]   Sönmez F K, Koemuescue A U, Erkan A, et al. 2005. An analysis of spatial and temporal dimension of drought vulnerability in Turkey using the standardized precipitation index. Natural Hazards, 35(2): 243-264.
doi: 10.1007/s11069-004-5704-7
[40]   Tall A. 2008. Application of the palmer drought severity index in east Slovakian lowland. Cereal Research Communications, 36: 1195-1198.
[41]   Tan C P, Yang J P, Li M. 2015. Temporal-spatial variation of drought indicated by SPI and SPEI in Ningxia Hui Autonomous Region, China. Atmosphere, 6(10): 1399-1421.
doi: 10.3390/atmos6101399
[42]   Tao R, Zhang K, 2020. PDSI-based analysis of characteristics and spatiotemporal changes of meteorological drought in China from 1982 to 2015. Water Resources Protection, 36(5): 50-56. (in Chinese with English abstract)
[43]   Tatli H, Türkeş M. 2011. Empirical orthogonal function analysis of the Palmer drought indices. Agricultural and Forest Meteorology, 151(7): 981-991.
doi: 10.1016/j.agrformet.2011.03.004
[44]   Tefera A S, Ayoade J O, Bello N J. 2019. Comparative analyses of SPI and SPEI as drought assessment tools in Tigray Region, Northern Ethiopia. SN Applied Sciences, 1: 1265, doi: 10.1007/s42452-019-1326-2.
doi: 10.1007/s42452-019-1326-2
[45]   Vicente-Serrano S M, Beguería S, López-Moreno J I. 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of Climate, 23(7): 1696-1718.
doi: 10.1175/2009JCLI2909.1
[46]   Wang H J, Chen Y N, Pan Y P, et al. 2019. Assessment of candidate distributions for SPI/SPEI and sensitivity of drought to climatic variables in China. International Journal of Climatology, 39(11): 4392-4412.
doi: 10.1002/joc.6081
[47]   World Meteorological Organization. 2012. Standardized precipitation index user guide. In: Svoboda M, Hayes M, Wood D. World Meteorological Organization No. 1090. Geneva: World Meteorological Organization.
[48]   Yang P, Xia J, Zhang Y Y, et al. 2018. Comprehensive assessment of drought risk in the arid region of Northwest China based on the global palmer drought severity index gridded data. Science of the Total Environment, 627: 951-962.
doi: 10.1016/j.scitotenv.2018.01.234
[49]   Zarei A, Asadi E, Ebrahimi A, et al. 2017. Comparison of meteorological indices for spatio-temporal analysis of drought in Chahrmahal-Bakhtiyari province in Iran. Croatian Meteorological Journal, 52: 13-26.
[50]   Zarei A R, Shabani A, Moghimi M M. 2021. Accuracy assessment of the SPEI, RDI and SPI drought indices in regions of Iran with different climate conditions. Pure and Applied Geophysics, 178(4): 1387-1403.
doi: 10.1007/s00024-021-02704-3
[51]   Zhang J, Sun F B, Lai W L, et al. 2019. Attributing changes in future extreme droughts based on PDSI in China. Journal of Hydrology, 573: 607-615.
doi: 10.1016/j.jhydrol.2019.03.060
[52]   Zhang Y J, Yu Z S, Niu H S. 2018. Standardized Precipitation Evapotranspiration Index is highly correlated with total water storage over China under future climate scenarios. Atmospheric Environment, 194: 123-133.
doi: 10.1016/j.atmosenv.2018.09.028
[53]   Zhou Y L, Zhou P, Jin J L, et al. 2022. Drought identification based on Palmer drought severity index and return period analysis of drought characteristics in Huaibei Plain China. Environmental Research, 212: 113163, doi: 10.1016/j.envres.2022.113163.
doi: 10.1016/j.envres.2022.113163
[54]   Zoljoodi M, Didevarasl A. 2013. Evaluation of spatial-temporal variability of drought events in Iran using palmer drought severity index and its principal factors (through 1951-2005). Atmospheric and Climate Sciences, 3(2): 193-207.
doi: 10.4236/acs.2013.32021
[1] WANG Xiangyu, XU Min, KANG Shichang, LI Xuemei, HAN Haidong, LI Xingdong. Comprehensive applicability evaluation of four precipitation products at multiple spatiotemporal scales in Northwest China[J]. Journal of Arid Land, 2024, 16(9): 1232-1254.
[2] YANG Jianhua, LI Yaqian, ZHOU Lei, ZHANG Zhenqing, ZHOU Hongkui, WU Jianjun. Effects of temperature and precipitation on drought trends in Xinjiang, China[J]. Journal of Arid Land, 2024, 16(8): 1098-1117.
[3] XU Wenjie, DING Jianli, BAO Qingling, WANG Jinjie, XU Kun. Improving the accuracy of precipitation estimates in a typical inland arid area of China using a dynamic Bayesian model averaging approach[J]. Journal of Arid Land, 2024, 16(3): 331-354.
[4] LIU Xinyu, LI Xuemei, ZHANG Zhengrong, ZHAO Kaixin, LI Lanhai. A CMIP6-based assessment of regional climate change in the Chinese Tianshan Mountains[J]. Journal of Arid Land, 2024, 16(2): 195-219.
[5] BAI Jizhou, LI Jing, RAN Hui, ZHOU Zixiang, DANG Hui, ZHANG Cheng, YU Yuyang. Influence of varied drought types on soil conservation service within the framework of climate change: insights from the Jinghe River Basin, China[J]. Journal of Arid Land, 2024, 16(2): 220-245.
[6] Mitiku A WORKU, Gudina L FEYISA, Kassahun T BEKETIE, Emmanuel GARBOLINO. Projecting future precipitation change across the semi-arid Borana lowland, southern Ethiopia[J]. Journal of Arid Land, 2023, 15(9): 1023-1036.
[7] BAI Miao, LI Zhanling, HUO Pengying, WANG Jiawen, LI Zhanjie. Propagation characteristics from meteorological drought to agricultural drought over the Heihe River Basin, Northwest China[J]. Journal of Arid Land, 2023, 15(5): 523-544.
[8] Reza DEIHIMFARD, Sajjad RAHIMI-MOGHADDAM, Farshid JAVANSHIR, Alireza PAZOKI. Quantifying major sources of uncertainty in projecting the impact of climate change on wheat grain yield in dryland environments[J]. Journal of Arid Land, 2023, 15(5): 545-561.
[9] ZHANG Lihua, GAO Han, WANG Junfeng, ZHAO Ruifeng, WANG Mengmeng, HAO Lianyi, GUO Yafei, JIANG Xiaoyu, ZHONG Lingfei. Plant property regulates soil bacterial community structure under altered precipitation regimes in a semi-arid desert grassland, China[J]. Journal of Arid Land, 2023, 15(5): 602-619.
[10] Sakine KOOHI, Hadi RAMEZANI ETEDALI. Future meteorological drought conditions in southwestern Iran based on the NEX-GDDP climate dataset[J]. Journal of Arid Land, 2023, 15(4): 377-392.
[11] ZHANG Yixin, LI Peng, XU Guoce, MIN Zhiqiang, LI Qingshun, LI Zhanbin, WANG Bin, CHEN Yiting. Temporal and spatial variation characteristics of extreme precipitation on the Loess Plateau of China facing the precipitation process[J]. Journal of Arid Land, 2023, 15(4): 439-459.
[12] LI Hongfang, WANG Jian, LIU Hu, MIAO Henglu, LIU Jianfeng. Responses of vegetation yield to precipitation and reference evapotranspiration in a desert steppe in Inner Mongolia, China[J]. Journal of Arid Land, 2023, 15(4): 477-490.
[13] Adnan ABBAS, Asher S BHATTI, Safi ULLAH, Waheed ULLAH, Muhammad WASEEM, ZHAO Chengyi, DOU Xin, Gohar ALI. Projection of precipitation extremes over South Asia from CMIP6 GCMs[J]. Journal of Arid Land, 2023, 15(3): 274-296.
[14] Mohsen SHARAFATMANDRAD, Azam KHOSRAVI MASHIZI. Evaluation of restoration success in arid rangelands of Iran based on the variation of ecosystem services[J]. Journal of Arid Land, 2023, 15(11): 1290-1314.
[15] LI Qian, MA Long, LIU Tingxi. Transformation among precipitation, surface water, groundwater, and mine water in the Hailiutu River Basin under mining activity[J]. Journal of Arid Land, 2022, 14(6): 620-636.