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Accuracy Assessment of the SPEI, RDI and SPI Drought Indices in Regions of Iran with Different Climate Conditions

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Abstract

Considering the impact of drought on agricultural products and human food security, the selection of the appropriate drought index to assess drought conditions is very important. Therefore, in this research, based on the relationship between the percent annual yield loss (AYL) of winter wheat (Triticum sativum) and three commonly used drought indices, i.e. Standardized Precipitation Evapotranspiration Index (SPEI), Reconnaissance Drought Index (RDI) and Standardized Precipitation Index (SPI), the accuracy of these indices was evaluated at 1-, 3-, 6- and 12-month time scales. Results showed that the average AYL at Ahvaz, Babolsar, Esfahan, Gorgan, Kerman, Mashhad, Ramsar, Rasht, Shiraz and Zabol was 66.64, 5.42, 97.52, 10.20, 98.57, 84.99, 2.47, 3.84, 77.03 and 97.07%, respectively. At stations with hyper-arid, semi-arid, Mediterranean, humid and hyper-humid type A climate conditions such as Zabol, Esfahan, Mashhad, Shiraz, Gorgan, Babolsar, Ramsar and Rasht stations, the calculated values of SPEI demonstrated the highest CC with AYL in winter wheat. At Kerman and Ahvaz, with arid climate conditions, the calculated values of the RDI had the greatest CC with AYL in winter wheat. Thus, in general, among the SPI, RDI and SPEI, the use of the SPEI is recommended for the assessment of drought characteristics.

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The data used in this research will be available from the corresponding author upon reasonable request.

References

  • Adnan, S., Ullah, K., Shuanglin, L., Gao, S., Hayat Khan, A., & Mahmood, R. (2018). Comparison of various drought indices to monitor drought status in Pakistan. Climate Dynamics, 51(5–6), 1885–1899.

    Article  Google Scholar 

  • Aguirre, A., Río, M. D., & Condés, S. (2018). Intra- and inter-specific variation of the maximum size-density relationship along an aridity gradient in Iberian pinewoods. Forest Ecology and Management, 411, 90–100.

    Article  Google Scholar 

  • Aiken, R. M., Lin, X., & Zambreski, Z. T. (2017). Winter wheat yield responses to climate variation in the US Central Great Plains. In 2017 ASABE annual international meeting (p. 1). American Society of Agricultural and Biological Engineers. https://doi.org/10.13031/aim.201701661.

  • Alizadeh, A. (2017). Applied hydrology. Ferdowsi University Press.

    Google Scholar 

  • Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. (1998). Crop evapotranspiration. Guidelines for Computing Crop Water Requirements, FAO Irrigation and Drainage Paper 56. FAO, Rome.

  • Bahrami, M., Bazrkar, S., & Zarei, A. R. (2018). Modeling, prediction and trend assessment of drought in Iran using standardized precipitation index. Journal of Water and Climate Change. https://doi.org/10.2166/wcc.2018.174.

    Article  Google Scholar 

  • Basal, O., & Szabó, A. (2020). The combined effect of drought stress and nitrogen fertilization on soybean. Agronomy, 10(3), 384.

    Article  Google Scholar 

  • Bista, D. R., Heckathorn, S. A., Jayawardena, D. M., & Boldt, J. K. (2020). Effect of drought and carbon dioxide on nutrient uptake and levels of nutrient-uptake proteins in roots of barley. American Journal of Botany, 107(10), 1401–1409.

    Article  Google Scholar 

  • De Martonne, E. (1926). Aérisme et indice d’aridité. Comptes rendus de l’Académie des Sciences, 182, 1395–1398.

    Google Scholar 

  • Elhag, K., & Zhang, W. (2018). Monitoring and assessment of drought focused on its impact on sorghum yield over Sudan by using meteorological drought indices for the period 2001–2011. Remote Sensing, 10(8), 1231.

    Article  Google Scholar 

  • Feng, P., Wang, B., Li Liu, D., Xing, H., Ji, F., Macadam, I., Ruan, H., & Yu, Q. (2018). Impacts of rainfall extremes on wheat yield in semi-arid cropping systems in eastern Australia. Climatic Change, 147(3–4), 555–569.

    Article  Google Scholar 

  • Gidey, E., Dikinya, O., Sebego, R., Segosebe, E., & Zenebe, A. (2018). Modeling the spatio-temporal meteorological drought characteristics using the standardized precipitation index (SPI) in Raya and its environs, Northern Ethiopia. Earth Systems and Environment. https://doi.org/10.1007/s41748-018-0057-7.

    Article  Google Scholar 

  • Ghorbani, M. A., Kazempour, R., Chau, K. W., Shamshirband, S. H., & Taherei Ghazvinei, P. (2018). Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: A case study in Talesh, Northern Iran. Engineering Applications of Computational Fluid Mechanics, 12(1), 724–737.

    Article  Google Scholar 

  • Jia y., Zhang B., Ma B, . (2018). Daily SPEI reveals long-term change in drought characteristics in Southwest China. Chinese Geographical Science, 28(4), 680–693.

    Article  Google Scholar 

  • Jokar, P., & Masoudi, M. (2018). Analyzing spatial pattern of drought in Iran, using Percent of Normal Index (PNI). Fresenius Environmental Bulletin, 27(4), 2011–2018.

    Google Scholar 

  • Li, C., Wang, J., Yin, S., Bao, Y., Li, Y., & Yn, S. (2018). Drought hazard assessment and possible adaptation options for typical steppe grassland in Xilingol League, Inner Mongolia, China. Theoretical and Applied Climatology. https://doi.org/10.1007/s00704-018-2563-9.

    Article  Google Scholar 

  • Liu, D., You, J., Xie, Q., Huang, Y., & Tong, H. (2018). Spatial and temporal characteristics of drought and flood in Quanzhou based on standardized precipitation index (SPI) in recent 55 years. Journal of Geoscience and Environment Protection, 6(08), 25–37.

    Article  Google Scholar 

  • Liu, X., Pan, Y., Zhu, X., Yang, T., Bai, J., & Sun, Z. (2018). Drought evolution and its impact on the crop yield in the North China Plain. Journal of Hydrology, 564, 984–996.

    Article  Google Scholar 

  • Ma, B., Zhang, B., Jia, L. & Huang, H. (2020). Conditional distribution selection for SPEI-daily and its revealed meteorological drought characteristics in China from 1961 to 2017. Atmospheric Research. https://doi.org/10.1016/j.atmosres.2020.105108.

  • Mahdavi, M. (2002). Applied hydrology. Tehran University Press.

    Google Scholar 

  • Marini, G., Fontana, N., & Mishra, A. K. (2018). Investigating drought in Apulia region, Italy using SPI and RDI. Theoretical and Applied Climatology. https://doi.org/10.1007/s00704-018-2604-4.

    Article  Google Scholar 

  • Masoudi, M., & Hakimi, S. (2014). A new model for vulnerability assessment of drought in Iran using Percent of Normal Precipitation Index (PNPI). Iranian Journal of Science and Technology (Sciences), 38(4), 435–440.

    Google Scholar 

  • Mathbout, S., Lopez-Bustins, J. A., Martin-Vide, J., Bech, J., & Rodrigo, F. S. (2018). Spatial and temporal analysis of drought variability at several time scales in Syria during 1961–2012. Atmospheric Research, 200, 153–168.

    Article  Google Scholar 

  • 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, 17–22 January (Vol. 17, No. 22, pp. 179–184). American Meteorological Society.

  • Merabti, A., Martins, D. S., Meddi, M., & Pereira, L. S. (2018). Spatial and time variability of drought based on SPI and RDI with various time scales. Water Resources Management, 32(3), 1087–1100.

    Article  Google Scholar 

  • Mitra, S., Srivastava, P., & Lamba, J. (2018). Probabilistic assessment of projected climatological drought characteristics over the Southeast USA. Climatic Change, 147(3–4), 601–615.

    Article  Google Scholar 

  • Mokarram, M., Hamzeh, S., Aminzadeh, F., & Zarei, A. R. (2015). Using machine learning for land suitability classification. West African Journal of Applied Ecology, 23(1), 63–73.

    Google Scholar 

  • Nguyen-Huy, T., Deo, R. C., Mushtaq, S., An-Vo, D. A., & Khan, S. (2018). Modeling the joint influence of multiple synoptic-scale, climate mode indices on Australian wheat yield using a vine copula-based approach. European Journal of Agronomy, 98, 65–81.

    Article  Google Scholar 

  • Nohtani, M., Ajorlo, M., & Sarhadi, M. (2018). Zoning drought with standardized precipitation index and reconnaissance drought index in Sistan and Baluchestan Province, Southeastern Iran. Ecopersia (IQBQ), 6(2), 111–119.

    Google Scholar 

  • Nosrati, K., & Zareiee, A. R. (2011). Assessment of meteorological drought using SPI in West Azarbaijan Province, Iran. Journal of Applied Sciences and Environmental Management, 15(4), 563–569.

    Google Scholar 

  • Páscoa, P., Gouveia, C. M., Russo, A., & Trigo, R. M. (2017). The role of drought on wheat yield interannual variability in the Iberian Peninsula from 1929 to 2012. International Journal of Biometeorology, 61(3), 439–451.

    Article  Google Scholar 

  • Peña-Gallardo, M., Vicente-Serrano, S. M., Domínguez-Castro, F., Quiring, S., Svoboda, M., Beguería, S., & Hannaford, J. (2018). Effectiveness of drought indices in identifying impacts on major crops across the USA. Climate Research, 75(3), 221–240.

    Article  Google Scholar 

  • Qasem, S. N., Samadianfard, S., Kheshtgar, S., Jarhan, S., Kis, O., Shamshirband, S., & Chau, K. W. (2019). Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Engineering Applications of Computational Fluid Mechanics, 13(1), 177–187.

    Article  Google Scholar 

  • Rao, A. S., Padhi, J., & Das, B. (2018). Assessment of drought in Balangir District of Odisha, India using drought indices. Climate Change Impacts. https://doi.org/10.1007/978-981-10-5714-4_21.

    Article  Google Scholar 

  • Ravinesh, C. D., Sancho, S. S., Leopoldo, C. C., & Beatriz, S. M. (2018). Drought prediction with standardized precipitation and evapotranspiration index and support vector regression models. Integrating Disaster Science and Management. https://doi.org/10.1016/B978-0-12-812056-9.00010-5.

    Article  Google Scholar 

  • Sebghati, M., Ahmadi, H., & Moghaddam, A. R. (2016). The calculation of continuity and intensity of droughts using Modified SPEI Index (case study: Tabriz and Urmia cities). Journal of Environment and Water Engineering, 2(2), 188–195.

    Google Scholar 

  • Shirshahi, F., Babazadeh, H., Ebrahimipak, N., & Zeraatkish, Y. (2018). Calibration and assessment of AquaCrop model for managing the quantity and time of applying wheat deficit irrigation. Irrigation Sciences and Engineering, 41(1), 31–44. https://doi.org/10.22055/jise.2018.13451 (in Persian with English abstract).

    Article  Google Scholar 

  • Sobral, B. S., Oliveira-Júnior, J. F., de Gois, G., & Pereira-Júnior, E. R. (2018). Spatial variability of SPI and RDIst drought indices applied to intense episodes of drought occurred in Rio de Janeiro State, Brazil. International Journal of Climatology, 38(10), 3896–3916.

    Article  Google Scholar 

  • Soukayna, M., Nawin, R., Ravinesh, C. D., & Jan, F. A. (2018). Input selection and data-driven model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone region. Atmospheric Research, 212, 130–149.

    Article  Google Scholar 

  • Tian, L., Yuan, S., & Quiring, S. M. (2018). Evaluation of six indices for monitoring agricultural drought in the south-central United States. Agricultural and Forest Meteorology, 249, 107–119.

    Article  Google Scholar 

  • Tigkas, D., & Tsakiris, G. (2015). Early estimation of drought impacts on rainfed wheat yield in Mediterranean climate. Environmental Processes, 2(1), 97–114.

    Article  Google Scholar 

  • Tigkas, D., Vangelis, H., & Tsakiris, G. (2018). Drought characterization based on an agriculture-oriented standardized precipitation index. Theoretical and Applied Climatology. https://doi.org/10.1007/s00704-018-2451-3.

    Article  Google Scholar 

  • Tirivarombo, S., Osupile, D., & Eliasson, P. (2018). Drought monitoring and analysis: Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI). Physics and Chemistry of the Earth. https://doi.org/10.1016/j.pce.2018.07.001.

    Article  Google Scholar 

  • Tsakiris, G., Pangalou, D., & Vangelis, H. (2007). Regional drought assessment based on reconnaissance drought index (RDI). Water Resources Management, 21(5), 821–833.

    Article  Google Scholar 

  • Vicente-Serrano, S. M., Beguería, S., & Lopez-Moreno, J. I. (2010). A multi-scalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index—SPEI. Journal of Climate, 23, 1696–1718.

    Article  Google Scholar 

  • Wable, P. S., Jha, M. K., & Shekhar, A. (2019). Comparison of drought indices in a semi-Arid River Basin of India. Water Resources Management, 33(1), 75–102.

    Article  Google Scholar 

  • Wang, S., Mo, X., Hu, S., Liu, S., & Liu, Z. (2018). Assessment of droughts and wheat yield loss on the North China Plain with an aggregate drought index (ADI) approach. Ecological Indicators, 87, 107–116.

    Article  Google Scholar 

  • Zarei, A. R. (2018). Evaluation of drought condition in arid and semi-arid regions, using RDI index. Water Resources Management, 32(5), 1689–1711. https://doi.org/10.1007/s11269-017-1898-9.

  • Zarei, A. R., & Mahmoudi, M. R. (2020a). Evaluation and comparison of the effectiveness rate of the various meteorological parameters on UNEP Aridity Index using backward multiple ridge regression. Water Resources Management. https://doi.org/10.1007/s11269-020-02716-z.

    Article  Google Scholar 

  • Zarei, A. R., & Mahmoudi, M. R. (2020b). Ability assessment of the stationary and cyclostationary time series models to predict drought indices. Water Resources Management. https://doi.org/10.1007/s11269-020-02710-5.

    Article  Google Scholar 

  • Zarei, A. R., Mahmoudi, M. R., Shabani, A., & Achite, M. (2020). Determination of the most important meteorological parameters affecting the yield and biomass of barley and winter wheat using the random forest algorithm. Paddy and Water Environment. https://doi.org/10.1007/s10333-020-00832-5.

    Article  Google Scholar 

  • Zarei, A. R., & Moghimi, M. M. (2019). Environmental assessment of semi-humid and humid regions based on modeling and forecasting of changes in monthly temperature. International Journal of Environmental Science and Technology, 16(3), 1457–1470.

    Article  Google Scholar 

  • Zarei, A. R., Shabani, A., & Mahmoudi, M. R. (2019). Comparison of the climate indices based on the relationship between yield loss of rain-fed winter wheat and changes of climate indices using GEE model. Science of the Total Environment, 661, 711–722.

    Article  Google Scholar 

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Acknowledgements

The authors thank the Iranian Meteorological Organization for their cooperation in providing the necessary data.

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  1. Department of Range and Watershed Management (Nature Engineering), College of Agricultural Science, Fasa University, Fasa, Iran

    Abdol Rassoul Zarei

  2. Department of Water Science Engineering, College of Agriculture, Fasa University, Fasa, Iran

    Ali Shabani & Mohammad Mehdi Moghimi

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  1. Abdol Rassoul Zarei
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Abdol Rassoul Zarei participated in the collection of data, analysis of the results and writing the article. Ali Shabani and Mohammad Mehdi Moghimi assisted in analyzing the results.

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Zarei, A.R., Shabani, A. & Moghimi, M.M. Accuracy Assessment of the SPEI, RDI and SPI Drought Indices in Regions of Iran with Different Climate Conditions. Pure Appl. Geophys. 178, 1387–1403 (2021). https://doi.org/10.1007/s00024-021-02704-3

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