Alhumaima, A.S. & Abdullaev, S.M. (2018). Preliminary assessment of hydrothermal risks in the Euphrates–Tigris basin: Droughts in Iraq. Bulletin of the South Ural State University, Series, Computational Mathematics and Software Engineering, 7(4), 41-58. https://doi.org/10.14529/cmse180403

Alhumaima, A.S. & Abdullaev, S.M. (2019). Landscape Approach to Normalized Difference Vegetation Index Forecast by Artificial Neural Network: Example of Diyala River Basin. Bulletin of the South Ural State University, Series Computer Technologies, Automatic Control & Radioelectronics, 19(3), 5-19. https://doi.org/10.14529/ctcr190301

Chen, S., Gan, T.Y., Tan, X., Shao, D. & Zhu, J. (2019). Assessment of CFSR, ERA-Interim, JRA-55, MERRA-2, NCEP-2 reanalysis data for drought analysis over China. Climate Dynamics, 53(1-2), 737-757. https://doi.org/10.1007/s00382-018-04611-1

Cullen, H.M., Kaplan, A., Arkin, P.A. & de Menocal, P.B. (2002). Impact of the North Atlantic Oscillation on Middle Eastern Climate and Streamflow. Climatic Change, 55(3), 315-338. https://doi.org/10.1023/A:1020518305517

Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A.C.M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A.J., Haimberger, L., Healy, S.B., Hersbach, H., Hólm, E.V., Isaksen, L., Kĺllberg, P., Köhler, M., Matricardi, M., McNally, A.P., Monge-Sans, B.M., Morcrette, J.J., Park, B.K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.N. & Vitart, F. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553-597. https://doi.org/10.1002/qj.828

Didan, K. (2015). MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid, V006 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD13Q1.006

Essou, G.R.C., Sabarly, F., Lucas-Picher, P., Brissette, F. & Poulin, A. (2016). Can precipitation and temperature from meteorological reanalyses be used for hydrological modeling? Journal of Hydrometeorology, 17(7), 1929-1950. https://doi.org/10.1175/JHM-D15-0138.1

Food and Agriculture Organization of the United Nations [FAO] (2009). AQUASTAT Transboundary River Basins – Euphrates-Tigris River Basin. Rome: Food and Agriculture Organization of the United Nations.

Gelaro, R., McCarty, W., Suárez, M.J., Todling, R., Molod, A., Takacs, L., Randles, C.A., Darmenov, A., Bosilovich, M.G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A., Gu, W., Kim, G.K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J.E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S.D., Sienkiewicz, M. & Zhao, B. (2017). The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Journal of Climate, 30(14), 5419-5454. https://doi.org/10.1175/JCLI-D16-0758.1

Harris, I., Jones, P.D., Osborn, T.J. & Lister, D.H. (2014). Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 Dataset. International Journal of Climatology, 34(3), 623-642. https://doi.org/10.1002/joc.3711

Kamble, B., Kilic, A. & Hubbard, K. (2013). Estimating Crop Coefficients Using Remote Sensing-Based Vegetation Index. Remote Sensing, 5(4), 1588-1602. https://doi.org/10.3390/rs5041588

Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.K., Hnilo, J.J., Fiorino, M. & Potter, G. L. (2002). NCEP–DOE AMIP-II Reanalysis (R-2). Bulletin of the American Meteorological Society, 83(11), 1631-1644. https://doi.org/10.1175/BAMS-83-11-1631

Karabörk, M.Ç. & Kahya, E. (2009). The links between the categorised Southern Oscillation indicators and climate and hydrologic variables in Turkey. Hydrological Processes, 23(13), 1927-1936. https://doi.org/10.1002/hyp.7331

Khidher, S.A. & Pilesjö, P. (2015). The effect of the North Atlantic Oscillation on the Iraqi climate 1982–2000. Theoretical and Applied Climatology, 122(3-4), 771-782. https://doi.org/10.1007/s00704-014-1327-4

Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, Ch., Endo, H., Miyaoka, K. & Takahashi, K. (2015). The JRA-55 reanalysis: general specifications and basic characteristics. Journal of the Meteorological Society of Japan. Ser. II, 93(1), 5-48. https://doi.org/10.2151/jmsj.2015-001

Latham, J., Cumani, R., Rosati, I. & Bloise, M. (2014). FAO Global Land Cover SHARE (GLC-SHARE) Beta-Release (version 1.0). Rome: Land and Water Division, Food and Agriculture Organization.

Li, F., Li, H., Lu, W., Zhang, G., & Kim, J.C. (2019). Meteorological Drought Monitoring in Northeastern China Using Multiple Indices. Water, 11(1), 72. https://doi.org/10.3390/w11010072

Luo, N., Mao, D., Wen, B. & Liu, X. (2020). Climate Change Affected Vegetation Dynamics in the Northern Xinjiang of China: Evaluation by SPEI and NDVI. Land, 9(3), 90. https://doi.org/10.3390/land9030090

Mckee, T.B., Doesken, N.J. & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, American Meteorological Society, 17(22), 179-184.

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. https://doi.org/10.1016/j.atmosres.2017.09.016

Ministry of Economy, Trade and Industry of Japan / United States National Aeronautics and Space Administration [METI/NASA] (2011). Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model – ASTER GDEM (version 2) [DEM data sets]. Retrieved from https://asterweb.jpl.nasa.gov/gdem.asp

Pourasghar, F., Oliver, E.C.J. & Holbrook, N.J. (2019). Modulation of wet-season rainfall over Iran by the Madden–Julian Oscillation, Indian Ocean Dipole and El Nińo–Southern Oscillation. International Journal of Climatology, 39(10), 4029-4040. https://doi.org/10.1002/joc.6057

Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.T., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M.P., van den Dool, H., Zhang, Q., Wang, W., Chen, M. & Becker, E. (2014). The NCEP Climate Forecast System Version 2. Journal of Climate, 27(6), 2185-2208. https://doi.org/10.1175/JCLI-D-12-00823.1

Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150. https://doi.org/10.1016/0034-4257(79)90013-0

Willmott, C.J. & Matsuura, K. (2019). Terrestrial Air Temperature and Precipitation: Monthly and Annual Time Series (1900-2017). Delaware: Department of Geography, University of Delaware.

Wu, D., Zhao, X., Liang, S., Zhou, T., Huang, K., Tang, B. & Zhao, W. (2015). Time-lag effects of global vegetation responses to climate change. Global Change Biology, 21(9), 3520-3531. https://doi.org/10.1111/gcb.12945

Xu, Y., Yang, J. & Chen, Y. (2016). NDVI-based vegetation responses to climate change in an arid area of China. Theoretical and Applied Climatology, 126(1-2), 213-222. https://doi.org/10.1007/s00704-015-1572-1

Yuan, W., Wu, S., Hou, S., Xu, Z. & Lu, H. (2019). Normalized Difference Vegetation Index-based assessment of climate change impact on vegetation growth in the humidarid transition zone in northern China during 1982–2013. International Journal of Climatology, 39(15), 5583-5598. https://doi.org/10.1002/joc.6172