The sensitivity of vegetation in the lower Tigris basin landscapes to regional and global climate variability

• Autorzy: Alhumaima Ali S. , Abdullaev Sanjar M.

Identyfikatory

DOI

DOI: 10.22630/PNIKS.2021.30.1.14

• Języki publikacji: EN

Abstrakty

EN

This study investigates the lower Tigris basin’s the normalized difference vegetation index (NDVI) sensitivity in 2000–2016 to regional climate variability reflected by the monthly precipitation and temperature time series of seven global datasets as well as to four global circulation indices. To examine the effect of climate variability on the different ecosystems, the study area has been classified into 10 smaller natural and anthropogenic landscapes based on landforms and land cover patterns. The preliminary analysis showed that the maximum biological productivity reflected by the NDVI of March and April has the highest correlation (0.5–0.8) to the same cumulative amounts of October–March period total precipitation and January–March period mean temperatures according to all datasets. In addition, this article showed there is a correlation between landscapes’ NDVI and global modulation represented by the September–February state of El Nińo-Southern Oscillation (ENSO) (0.55–0.70) and December state of the dipole mode index (DMI) (0.35–0.72). The significant differences in the original precipitation and temperature levels according to the different datasets have urged the use of normalized time series: z-score of temperatures and analogous six-months the standardized precipitation index (SPI). However, the multiple correlation analysis showed that using ERA-

Słowa kluczowe

EN

climate variability; vegetation; global modulation; precipitation; temperature

• Wydawca: Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
• Czasopismo: Przegląd Naukowy Inżynieria i Kształtowanie Środowiska
• Rocznik: 2021
• Tom: Vol. 30, No. 1
• Strony: 159--170
• Opis fizyczny: Bibliogr. 28 poz., rys., tab., wykr.

Twórcy

autor

Alhumaima Ali S.

• South Ural State University, Department of System Programming, Lenin prospekt, 76, Chelyabinsk 454080, Russian Federation

• https://orcid.org/0000-0003-0895-6413

autor

Abdullaev Sanjar M.

• South Ural State University, Department of System Programming, Lenin prospekt, 76, Chelyabinsk 454080, Russian Federation

• https://orcid.org/0000-0003-0405-704X

Bibliografia

• 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