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Spatiotemporal monitoring of droughts in Iran using remote-sensing indices

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Abstract

This study spatially monitored drought in Iran using  drought indicators. Four drought indicators measured from 2016 to 2020 were used: temperature condition index (TCI), vegetation condition index (VCI), vegetation health index (VHI), and precipitation condition index (PCI). Moreover, a standardized precipitation index (SPI) was prepared using rainfall measurements from 1989 to 2019. The TCI revealed that most of Iran was classified as having “severe drought” in 2020. The highest value of VCI showed for northern Iran, which belongs to the class without drought.   The VHI indicated that vegetation stress increased over the study period throughout the region, and areas of severe and moderate drought reached their greatest extents in the aforementioned years. Significant droughts occurred in central, eastern, and southeastern Iran, and mild droughts occurred in northern Iran. The PCI indicated that rainfall amounts have diminished in most of the country over the period of study. The 30-year SPI showed that northern Iran received fine rain and the region has parts that can be classified as either extremely wet or very wet. However, most of the country was extremely dry and severely dry. The analysis of the VHI index for agricultural plants showed that 27.71% of Iran's agricultural regions, including the provinces of Razavi Khorasan, Hamadan, and Khozestan, experienced “critical drought” conditions. The study provides guidance for the selection of the most useful drought-monitoring indicators and can enable a more thorough understanding of drought in arid and semiarid regions.

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References

  • Abeysingha NS, Rajapaksha UR (2020) SPI-based spatiotemporal drought over Sri Lanka. In: Advances in meteorology

  • AghaKouchak A, Nakhjiri N (2012) A near real-time satellite-based global drought climate data record. Environ Res Lett 7(4):044037

    Article  Google Scholar 

  • AghaKouchak A, Feldman D, Hoerling M, Huxman T, Lund J (2015) Water and climate: recognize anthropogenic drought. Nat News 524(7566):409

    Article  Google Scholar 

  • Agutu NO, Awange JL, Zerihun A, Ndehedehe CE, Kuhn M, Fukuda Y (2017) Assessing multi-satellite remote sensing, reanalysis, and land surface models’ products in characterizing agricultural drought in East Africa. Remote Sens Environ 194:287–302

    Article  Google Scholar 

  • Alborzi A, Mirchi A, Moftakhari H, Mallakpour I, Alian S, Nazemi A, Hassanzadeh E, Mazdiyasni O, Ashraf S, Madani K, Norouzi H (2018) Climate-informed environmental inflows to revive a drying lake facing meteorological and anthropogenic droughts. Environ Res Lett 13(8):084010

    Article  Google Scholar 

  • Ali S, Henchiri M, Yao F, Zhang J (2019) Analysis of vegetation dynamics, drought in relation with climate over South Asia from 1990 to 2011. Environ Sci Pollut Res 26(11):11470–11481

    Article  Google Scholar 

  • Andreadis KM, Clark EA, Wood AW, Hamlet AF, Lettenmaier DP (2005) Twentieth-century drought in the conterminous United States. J Hydrometeorol 6(6):985–1001

    Article  Google Scholar 

  • Araste M, Kaboli H, Yazdani M (2017) Assessing the impacts of meteorological drought on yield of rainfed wheat and barley (case study: Khorasan Razavi province). J Agric Meteorol 5(1):15–25

    Google Scholar 

  • Bajgiran PR, Darvishsefat AA, Khalili A, Makhdoum MF (2008) Using AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran. J Arid Environ 72(6):1086–1096

    Article  Google Scholar 

  • Boken VK (2009) Improving a drought early warning model for an arid region using a soil-moisture index. Appl Geogr 29(3):402–408

    Article  Google Scholar 

  • Du L, Tian Q, Yu T, Meng Q, Jancso T, Udvardy P, Huang Y (2013) A comprehensive drought monitoring method integrating MODIS and TRMM data. Int J Appl Earth Obs Geoinf 23:245–253

    Google Scholar 

  • Ebrahimy H, Naboureh A, Feizizadeh B, Aryal J, Ghorbanzadeh O (2021) Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for boosting land cover classification accuracy. Appl Sci 11(21):10309

    Article  Google Scholar 

  • Eskandari S, Reza Jaafari M, Oliva P, Ghorbanzadeh O, Blaschke T (2020) Mapping land cover and tree canopy cover in Zagros forests of Iran: application of Sentinel-2, Google Earth, and field data. Remote Sens 12(12):1912

    Article  Google Scholar 

  • FAO (2020) Drought and agriculture. FAO, Rome

    Google Scholar 

  • Feizizadeh B, Omarzadeh D, Kazemi Garajeh M, Lakes T, Blaschke T (2021) Machine learning data-driven approaches for land use/cover mapping and trend analysis using Google Earth Engine. J Environ Plan Manag 2021:1–33

    Google Scholar 

  • Ghadami M, Raziei T, Amini M, Modarres R (2020) Regionalization of drought severity–duration index across Iran. Nat Hazards 103(3):2813–2827

    Article  Google Scholar 

  • Gidey E, Dikinya O, Sebego R, Segosebe E, Zenebe A (2018) Analysis of the long-term agricultural drought onset, cessation, duration, frequency, severity and spatial extent using Vegetation Health Index (VHI) in Raya and its environs, Northern Ethiopia. Environ Syst Res 7(1):1–18

    Article  Google Scholar 

  • Golian S, Mazdiyasni O, AghaKouchak A (2015) Trends in meteorological and agricultural droughts in Iran. Theoret Appl Climatol 119(3):679–688

    Article  Google Scholar 

  • Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27

    Article  Google Scholar 

  • Guo H, Bao A, Liu T, Ndayisaba F, Jiang L, Zheng G, Chen T, De Maeyer P (2019) Determining variable weights for an optimal scaled drought condition index (OSDCI): evaluation in central Asia. Remote Sens Environ 231:111220

    Article  Google Scholar 

  • Han X, Wu J, Zhou H, Liu L, Yang J, Shen Q, Wu J (2020a) Intensification of historical drought over China based on a multi-model drought index. Int J Climatol 40(12):5407–5419

    Article  Google Scholar 

  • Han Y, Li Z, Huang C, Zhou Y, Zong S, Hao T, Niu H, Yao H (2020b) Monitoring droughts in the Greater Changbai Mountains using multiple remote sensing-based drought indices. Remote Sens 12(3):530

    Article  Google Scholar 

  • Honble P, Patil PP, Kumar S, Deore SJ, Bhatt EB, Patil SU, Jadhav PDV, Sonawane MJ (2019) Disaster management: new challenges and solutions

  • Iranian Ministry of Energy (2018) Annual water resources report. IME publication, 350pp

  • Jafari SM, Nikoo MR, Dehghani M, Alijanian M (2020) Evaluation of two satellite-based products against ground-based observation for drought analysis in the southern part of Iran. Nat Hazards 102(3):1249–1267

    Article  Google Scholar 

  • Jain SK, Keshri R, Goswami A, Sarkar A (2010) Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India. Nat Hazards 54(3):643–656

    Article  Google Scholar 

  • Kaboli S, Hekmatzadeh AA, Darabi H et al (2021) Variation in physical characteristics of rainfall in Iran, determined using daily rainfall concentration index and monthly rainfall percentage index. Theor Appl Climatol 144:507–520 (2021). https://doi.org/10.1007/s00704-021-03553-9

    Article  Google Scholar 

  • Kamali B, Houshmand Kouchi D, Yang H, Abbaspour KC (2017) Multilevel drought hazard assessment under climate change scenarios in semi-arid regions—a case study of the Karkheh river basin in Iran. Water 9(4):241

    Article  Google Scholar 

  • Khan R, Gilani H (2021) Global drought monitoring with big geospatial datasets using Google Earth Engine. Environ Sci Pollut Res 28(14):17244–17264

    Article  Google Scholar 

  • Khazaei H, Subedi M, Nickerson M, Martínez-Villaluenga C, Frias J, Vandenberg A (2019) Seed protein of lentils: current status, progress, and food applications. Foods 8(9):391

    Article  Google Scholar 

  • Kogan FN (1995) Application of vegetation index and brightness temperature for drought detection. Adv Space Res 15(11):91–100

    Article  Google Scholar 

  • Kogan F, Gitelson A, Zakarin E, Spivak L, Lebed L (2003) AVHRR-based spectral vegetation index for quantitative assessment of vegetation state and productivity. Photogramm Eng Remote Sens 69(8):899–906

    Article  Google Scholar 

  • Kogan F, Adamenko T, Guo W (2013) Global and regional drought dynamics in the climate warming era. Remote Sens Lett 4(4):364–372

    Article  Google Scholar 

  • Kogan F, Guo W, Strashnaia A, Kleshenko A, Chub O (2016) Modelling and prediction of crop losses from NOAA polar-orbiting operational satellites. Geomatics Nat Hazards Risk 7(3):886–900

    Article  Google Scholar 

  • Koohi S, Azizian A, Brocca L (2021) Spatiotemporal drought monitoring using bottom-up precipitation dataset (SM2RAIN-ASCAT) over different regions of Iran. Sci Total Environ 779:146535

    Article  Google Scholar 

  • Liu WT, Kogan FN (1996) Monitoring regional drought using the vegetation condition index. Int J Remote Sens 17(14):2761–2782

    Article  Google Scholar 

  • Liu X, Zhu X, Pan Y, Li S, Liu Y, Ma Y (2016) Agricultural drought monitoring: progress, challenges, and prospects. J Geog Sci 26(6):750–767

    Article  Google Scholar 

  • Lobell DB, Thau D, Seifert C, Engle E, Little B (2015) A scalable satellite-based crop yield mapper. Remote Sens Environ 164:324–333

    Article  Google Scholar 

  • Maghrebi M, Noori R, Bhattarai R, Yaseen ZM, Tang Q, Al-Ansari N, Danandeh-Mehr A, Karbassi A, Omidvar J, Farnoush H, Torabi-Haghighi A (2020) Iran’s agriculture in the anthropocene. Earth’s Future 8: 2020EF001547

  • Magno R, Angeli L, Chiesi M, Pasqui M (2014) Prototype of a drought monitoring and forecasting system for the Tuscany region. Adv Sci Res 11(1):7–10

    Article  Google Scholar 

  • McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th conference on applied climatology, vol 17, no 22, pp 179–183

  • McVicar TR, Jupp DL (1998) The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: a review. Agric Syst 57(3):399–468

    Article  Google Scholar 

  • Mirnezami SJ, Bagheri A, Maleki A (2018) Inaction of society on the drawdown of groundwater resources: a case study of Rafsanjan plain in Iran. Water Altern 11(3):725–748

    Google Scholar 

  • Mirzaei A, Saghafian B, Mirchi A, Madani K (2019) The groundwater-energy-food nexus in Iran’s agricultural sector: implications for water security. Water 11(9):1835

    Article  Google Scholar 

  • Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1–2):202–216

    Article  Google Scholar 

  • Modarres R, Sarhadi A, Burn DH (2016) Changes of extreme drought and flood events in Iran. Global Planet Change 144:67–81

    Article  Google Scholar 

  • Moussa Kourouma J, Eze E, Negash E, Phiri D, Vinya R, Girma A, Zenebe A (2021) Assessing the spatio-temporal variability of NDVI and VCI as indices of crops productivity in Ethiopia: a remote sensing approach. Geomat Nat Haz Risk 12(1):2880–2903

    Article  Google Scholar 

  • Nam WH, Hayes MJ, Svoboda MD, Tadesse T, Wilhite DA (2015) Drought hazard assessment in the context of climate change for South Korea. Agric Water Manag 160:106–117

    Article  Google Scholar 

  • Nodefarahani M, Aradpour S, Noori R, Tang Q, Partani S, Klöve B (2020) Metal pollution assessment in surface sediments of Namak Lake, Iran. Environ Sci Pollut Res 27(36):45639–45649

    Article  Google Scholar 

  • Notti D, Cignetti M, Godone D, Giordan D (2022) Semi-automatic mapping of shallow landslides using free Sentinel-2 and Google Earth Engine. In: Natural hazards and earth system sciences discussions, pp 1–34

  • OCHA (Office for the Coordination of Humanitarian Affairs) (2000) United Nations technical mission on the drought situation in the Islamic Republic of Iran. https://reliefweb.int/sites/reliefweb.int/files/res

  • OCHA (Office for the Coordination of Humanitarian Affairs) (2001) Iran—Drought OCHA situation report no. 1. (Ref.: OCHA/GVA-2001/0135). https://reliefweb.int/node/83752

  • Park S, Im J, Park S, Rhee J (2017) Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula. Agric For Meteorol 237:257–269

    Article  Google Scholar 

  • Phiri D, Simwanda M, Salekin S, Nyirenda VR, Murayama Y, Ranagalage M (2020) Sentinel-2 data for land cover/use mapping: a review. Remote Sens 12(14):2291

    Article  Google Scholar 

  • Quiring SM, Ganesh S (2010) Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas. Agric For Meteorol 150(3):330–339

    Article  Google Scholar 

  • Rahaman SA, Venkatesh R (2020) Application of remote sensing and google earth engine for monitoring environmental degradation in the Nilgiri biosphere reserve and its ecosystem of Western Ghats, India. Int Arch Photogr Remote Sens Spatial Inf Sci 43:933–940

    Article  Google Scholar 

  • Rahimi J, Ebrahimpour M, Khalili A (2013) Spatial changes of extended De Martonne climatic zones affected by climate change in Iran. Theoret Appl Climatol 112(3):409–418

    Article  Google Scholar 

  • Raziei T, Saghafian B, Paulo AA, Pereira LS, Bordi I (2009) Spatial patterns and temporal variability of drought in western Iran. Water Resour Manage 23(3):439–455

    Article  Google Scholar 

  • Rhee J, Im J (2017) Meteorological drought forecasting for ungauged areas based on machine learning: using long-range climate forecast and remote sensing data. Agric for Meteorol 237:105–122

    Article  Google Scholar 

  • Rhee J, Im J, Carbone GJ (2010) Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sens Environ 114(12):2875–2887

    Article  Google Scholar 

  • Sazib N, Mladenova I, Bolten J (2018) Leveraging the google earth engine for drought assessment using global soil moisture data. Remote Sens 10(8):1265

    Article  Google Scholar 

  • Shahabfar A, Eitzinger J (2013) Spatio-temporal analysis of droughts in semi-arid regions by using meteorological drought indices. Atmosphere 4(2):94–112

    Article  Google Scholar 

  • Singh RP, Roy S, Kogan F (2003) Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. Int J Remote Sens 24(22):4393–4402

    Article  Google Scholar 

  • Sobrino JA, Jimenez-Munoz JC, Paolini L (2004) Land surface temperature retrieval from LANDSAT TM 5. Remote Sens Environ 90(4):434–440

    Article  Google Scholar 

  • Sruthi S, Aslam MM (2015) Agricultural drought analysis using the NDVI and land surface temperature data; a case study of Raichur district. Aquatic Procedia 4:1258–1264

    Article  Google Scholar 

  • Sun Y, Liao J, Zou X, Xu X, Yang J, Chen HY, Ruan H (2020) Coherent responses of terrestrial C: N stoichiometry to drought across plants, soil, and microorganisms in forests and grasslands. Agric For Meteorol 292:108104

    Article  Google Scholar 

  • Tabari H, Somee BS, Zadeh MR (2011) Testing for long-term trends in climatic variables in Iran. Atmos Res 100(1):132–140

    Article  Google Scholar 

  • Tabari H, Nikbakht J, Hosseinzadeh Talaee P (2013) Hydrological drought assessment in Northwestern Iran based on streamflow drought index (SDI). Water Resour Manage 27(1):137–151

    Article  Google Scholar 

  • Tavakkoli Piralilou S, Einali G, Ghorbanzadeh O, Nachappa TG, Gholamnia K, Blaschke T, Ghamisi P (2022) A Google Earth Engine approach for wildfire susceptibility prediction fusion with remote sensing data of different spatial resolutions. Remote Sens 14(3):672

    Article  Google Scholar 

  • Thavorntam W, Tantemsapya N, Armstrong L (2015) A combination of meteorological and satellite-based drought indices in a better drought assessment and forecasting in Northeast Thailand. Nat Hazards 77(3):1453–1474

    Article  Google Scholar 

  • Tian L, Yuan S, Quiring SM (2018) Evaluation of six indices for monitoring agricultural drought in the south-central United States. Agric For Meteorol 249:107–119

    Article  Google Scholar 

  • Tian W, Bai P, Wang K, Liang K, Liu C (2020) Simulating the change of precipitation-runoff relationship during drought years in the eastern monsoon region of China. Sci Total Environ 723:138172

    Article  Google Scholar 

  • Tucker CJ (1989) Comparing SMMR and AVHRR data for drought monitoring. Int J Remote Sens 10(10):1663–1672

    Article  Google Scholar 

  • Udelhoven T, Stellmes M, del Barrio G, Hill J (2009) Assessment of rainfall and NDVI anomalies in Spain (1989–1999) using distributed lag models. Int J Remote Sens 30(8):1961–1976

    Article  Google Scholar 

  • Unganai LS, Kogan FN (1998) Drought monitoring and corn yield estimation in Southern Africa from AVHRR data. Remote Sens Environ 63(3):219–232

    Article  Google Scholar 

  • USDA (2008) Iran: 2008/09 wheat production declines due to drought, Commodity Intelligence Report, Foreign Agricultural Services. https://ipad.fas.usda.gov/highlights/2008/05/

  • Venkatappa M, Sasaki N, Han P, Abe I (2021) Impacts of droughts and floods on croplands and crop production in Southeast Asia–An application of Google Earth Engine. Sci Total Environ 795:148829

    Article  Google Scholar 

  • Wang M, Lu D (2005) Land surface temperature database in clear days retrieved by using GMS 5 satellite observation: preliminary investigation. In: Optical technologies for atmospheric, ocean, and environmental studies, vol 5832, SPIE, pp 420–430

  • Wang K, Li T, Wei J (2019) Exploring drought conditions in the three river headwaters region from 2002 to 2011 using multiple drought indices. Water 11(2):190

    Article  Google Scholar 

  • Wardlow BD, Anderson MC, Verdin JP (eds) (2012) Remote sensing of drought: innovative monitoring approaches. CRC Press

  • Yazdani S, Haghsheno M (2008) Drought management and recommended solutions on how to deal with droughts. Am Euras J Agric Environ Sci 2:64–68

    Google Scholar 

  • Yulistya VD, Wibowo A, Kusratmoko E (2019) Assessment of agricultural drought in paddy field area using Vegetation Condition Index (VCI) in Sukaresmi District, Cianjur Regency. In: IOP conference series: earth and environmental science, vol 311, no 1, IOP Publishing, p 012020

  • Zhang A, Jia G (2013) Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sens Environ 134:12–23

    Article  Google Scholar 

  • Zhang P, Deschenes O, Meng K, Zhang J (2018) Temperature effects on productivity and factor reallocation: Evidence from a half million Chinese manufacturing plants. J Environ Econ Manag 88:1–17

    Article  Google Scholar 

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Acknowledgements

This study was supported by Iran National Science Foundation (INSF) (Grant No. 99011991). Thank you to Shiraz University and Center for Intelligent Agricultural Monitoring and Management of Iran for rain data.

Funding

The funding was provided by INSF (Grand No. 99011991).

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Authors and Affiliations

  1. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, 71441-65186, Iran

    Soheila Pouyan & Hamid Reza Pourghasemi

  2. Department of GIS/RS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mojgan Bordbar

  3. Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “Luigi Vanvitelli”, Via Vivaldi 43, 81100, Caserta, Italy

    Mojgan Bordbar

  4. Department of Geography, M. Tech Geoinformatics, Bharathidasan University, Palkalaiperur, Tiruchirappalli, Tamil Nadu, 620024, India

    Venkatesh Ravichandran

  5. Department of Geography and Environmental Studies, Texas State University, San Marcos, TX, USA

    John P. Tiefenbacher

  6. Center for Intelligent Agricultural Monitoring and Management of Iran, Agriculture Ministry of Iran, Jahad, Iran

    Mehrzad Kherad

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  1. Soheila Pouyan
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Contributions

SP, MB, VR, JPT, MK, and HRP designed the experiments, ran models, analyzed the results, and wrote and reviewed the manuscript.

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Correspondence to Hamid Reza Pourghasemi.

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Pouyan, S., Bordbar, M., Ravichandran, V. et al. Spatiotemporal monitoring of droughts in Iran using remote-sensing indices. Nat Hazards 117, 1–24 (2023). https://doi.org/10.1007/s11069-023-05847-9

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